• Corrosion Science and Technology
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• v.22 no.1
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• pp.55-63
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• 2023

The aim of this study was to propose a multiple linear regression (MLR) equation to predict ultimate tensile strength (UTS) of 316L stainless steel with unspecified pit corrosion. Tensile specimens with pit corrosion were prepared using a potentiostatic acceleration test method. Pit corrosion was characterized by measuring ten factors using a confocal laser microscope. Data were collected from 22 tensile tests. At 85% confidence level, total pit volume, maximum pit depth, mean ratio of surface area, and mean area were significant factors showing linear relationships with UTS. The MLR equation using these three significant factors at a 85% confidence level showed considerable prediction performance for UTS. Determination coefficient (R²) was 0.903 with training and test data sets. The yield strength ratio of 316L stainless steel was found to be around 0.85. All specimens with a pit corrosion presented a yield ratio of approximately 0.85 with R² of 0.998. Therefore, pit corrosion did not affect the yield ratio.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.397-407
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• 2022

The informal economy is a large part of the urban economy. The informal economy accounts for about half of Pakistan's GDP. This research examined nine different areas of Rawalpindi's Central Business District's business sector (CBD). A survey of 404 respondents from 16 CBD marketplaces enables a comprehensive examination of who works in the informal and formal economic sectors, how much they earn, their goals, perception of their job, and their degree of similarity to the rest of the working population. Furthermore, the statistics illustrate the pro-cyclical connections between the informal economic sector and the formal economy. The Multinomial Logistic Regression (MLR) technique is used for the analysis. The MLR results indicated the informal economic sector holds positive relation with earning members in a family, business expertise, average business sale, and negative relation with education level, satisfaction with government tax policies, household expense, and average investment in the business. From a resilience standpoint, governance is considered an intentional collective action to preserve a stable system condition. Hence, the current study recommends tax reforms and government institution reorganization to mobilize the informal sector and make effective institutional governance.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.393-393
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• 2021

도시화는 도시 호우 유출 발생으로 인한 수질 악화를 초래했고 문제를 해결하기 위해 본 연구에서는 보다 정확한 설계를 위해 그린인프라(Green Infrastructure, GI)의 구조적 특성과 수문학적인 특성을 이용해 어떤 인자들이 설계에 필요한지 상관관계를 통해 분석하였다. GI의 종류 중 저류지와 저류연못의 총부유사량(Total Suspended Solids, TSS)와 총인 (Total Phosphorous, TP)의 유입수, 유출수, 비점오염원 농도, 수문학적인 특성 그리고 GI의 구조적 특성을 Ordinary Least Squares regression(OLS)과 Multi Linear Regression(MLR) 방법을 적용하였다. GI의 구조적인 특성은 한 BMP마다 달라지지 않으나 호우사상의 데이터 개수에 의한 편향이 있을 수 있다. 이런 문제를 해결하기 위해 일정한 범위를 가지고 무작위로 데이터를 추출하는 방법과 이상치를 제외하는 방법을 사용하여 모델에 적용하였다. 이러한 OLS와 MLR 모델들의 정확도를 PBIAS(Percent Bias), NSE(Nash-Sutcliffe efficiency), RSR(RMSE-observations standard deviation ratio)을 통해 분석할 수 있다. 연구 결과 유입수의 비점오염원의 농도뿐만 아니라 수문학적 특성과 GI의 구조적 특성이 함께 들어갈 시 더 좋은 상관관계를 가지고 있음을 알 수 있다. 저류지가 저류연못보다 모델의 성능평가 면에서 좋은 값을 가지고 있지만 특성별 상관관계는 저류연못이 더 뚜렷한 결과를 보여준다.

• Korean Journal of Soil Science and Fertilizer
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• v.33 no.4
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• pp.221-233
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• 2000

Evaluation method of soil fertility by application of geographic information system (GIS) which includes landscape characteristics and soil map data was investigated from productivities of red pepper and tobacco grown on the fields with no fertilization. Total 131 fields experiments, 64 fields of red pepper and 67 fields of tobacco were conducted from 22 and 23 fields for red pepper and tobacco, respectively, located at Cheangweon and Eumseong counties in 1996, from 20 and 25 fields at Boeun and Goesan counties in 1997, and 22 and 19 fields at Jincheon and Chungju counties in 1998. All the experimental sites were selected on the basis of wide range of distribution in landscape and soil attributes. Dry weights and nutrients (N, P and K) uptakes by red pepper plant and tobacco leaves were considered as basic fertility of the soil (BFS). The BFS was estimated by twenty-five independent variables including 13 chemical properties and 12 GIS data. Twenty-five independent variables were classified by two groups, 15 quantitative variables and 10 qualitative variables, and were analyzed by multiple linear regression (MLR) of REG and GLM models of SAS. Dry weight of red pepper (DWRP) and dry weight of tobacco leaves (DWTL) every year showed high variations by five times in difference plots with minimum yield and maximum yield indicating the diverse soil fertility among the experimental fields. Evaluation for the BFS by the MLR including independent variables was better than that by simple regression showing gradual improvement by adding chemical properties, quantitative variables, and qualitative variables of the GIS. However the evaluation for the BFS by the MLR showed the better result for tobacco than red pepper. For example the variability in the DWTL by MLR was explained 34.2% by only chemical properties, 35.0% by adding quantitative variables, and 72.5% by adding both the quantitative and qualitative variables of the GIS compared with 21.7% by simple regression with $NO_3-N$ content in soil. Consequently, it is assumed that this approach by the MLR including both the quantitative and qualitative variables was available as an evaluation model of soil fertility for upland field.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Fire Science and Engineering
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• v.28 no.3
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• pp.55-61
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• 2014

This study was performed to test the combustive properties of pinus rigida specimens treated with methylpiperazinomethyl-bis-phosphonic acid $M^{n+}$ ($PIPEABPM^{n+}$)s and methylpiperazinomethyl-bis-phosphonic acid (PIPEABP). Pinus rigida Plates were painted in three times with 15 wt% $PIPEABPM^{n+}s$ and PIPEABP solutions at the room temperature respectively. After drying specimen treated with chemicals, combustive properties were examined by the cone calorimeter (ISO 5660-1). It was indicated that the specimens treated with $PIPEABPM^{n+}s$ showed the lower speed to peak mass loss rate ($MLR_{peak}$), (0.104~0.121) g/s than that of PIPEABP plate. In adition, the specimens treated with $PIPEABPM^{n+}s$ showed both the lower total smoke release rate (TSRR), (224.4~484.0) $m^2/m^2$ and $CO_{mean}$ production (0.0537~0.0628) kg/kg than those of PIPEAB plate. Especially, for the specimens treated with $PIPEABPM^{n+}$ by reducing the smoke production rate except 2nd-smoke production rate (2nd-SPR), (0.0254~0.02270) g/s treated with $PIPEABPNi^{2+}$, 2nd-SPR (0.0117~0.0146) g/s was lower than that of PIPEABP plate. Thus, It is supposed that the combustion-retardation properties were improved by the partial due to the treated $PIPEABPM^{n+}s$ in the virgin plate.

• Applied Chemistry for Engineering
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• v.26 no.4
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• pp.505-510
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• 2015

This study was performed to test the combustive properties of pinus rigida specimens treated with methylpiperazinomethyl-bis-phosphonic acid $M^{n+}$ ($PIPEABPM^{n+}$)s and methylpiperazinomethyl-bis-phosphonic acid (PIPEABP). Each pinus rigida plates were painted three times with 15 wt% $PIPEABPM^{n+}s$ or PIPEABP solutions at the room temperature. After drying specimens treated with chemicals, combustive properties were examined by the cone calorimeter (ISO 5660-1). It was indicated that the speed to peak mass loss rate ($MLR_{peak}$), (0.104~0.121) g/s for specimens treated with $PIPEABPM^{n+}s$ was lower than that of PIPEABP plate. In addition, the total smoke release rate (TSRR), $(224.4{\sim}484.0)m^2/m^2$ for $PIPEABPM^{n+}s$ treated specimens except specimen treated with PIPEABPAl3+ and $CO_{mean}$ production (0.0537~0.0628) kg/kg was smaller than that of PIPEABP plate. In particular, for the specimens treated with $PIPEABPM^{n+}$ by reducing the smoke production rate, the second-smoke production rate (2nd-SPR) $(0.0117{\sim}0.0146)m^2/s$ was lower than that of PIPEABP plate. It can thus be concluded that combustion-retardation properties of the treated $PIPEABPM^{n+}s$ were partially improved compared to those of the virgin plate.

• Analytical Science and Technology
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• v.24 no.6
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• pp.533-543
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• 2011

The hERG (human ether-a-go-go related gene) ion channel is a main factor for cardiac repolarization, and the blockade of this channel could induce arrhythmia and sudden death. Therefore, potential hERG ion channel inhibitors are now a primary concern in the drug discovery process, and lots of efforts are focused on the minimizing the cardiotoxic side effect. In this study, $IC_{50}$ data of 202 organic compounds in HEK (human embryonic kidney) cell from literatures were used to develop predictive 2D-QSAR model. Multiple linear regression (MLR), Support Vector Machine (SVM), and artificial neural network (ANN) were utilized to predict inhibition concentration of hERG ion channel as machine learning methods. Population based-forward selection method with cross-validation procedure was combined with each learning method and used to select best subset descriptors for each learning algorithm. The best model was ANN model based on 14 descriptors ($R^2_{CV}$=0.617, RMSECV=0.762, MAECV=0.583) and the MLR model could describe the structural characteristics of inhibitors and interaction with hERG receptors. The validation of QSAR models was evaluated through the 5-fold cross-validation and Y-scrambling test.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1083-1091
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• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.6
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• pp.43-54
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• 2018

The accurate estimation of reference crop evapotranspiration ($ET_o$) is essential in irrigation water management to assess the time-dependent status of crop water use and irrigation scheduling. The importance of $ET_o$ has resulted in many direct and indirect methods to approximate its value and include pan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs) have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, pattern recognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalized regression neural network (GRNN), to evaluate their capability to accurately predict $ET_o$ using daily meteorological data. All data were obtained from two automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily $ET_o$ was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of $ET_o$ and corresponding meteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against $ET_o$ calculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performed best followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers and policy makers for effective agricultural water governance.