Earticle

This study aims to explore the factors that influence Korean cosmetics consumption behavior of women in Ho Chi Minh City. The proposed research model includes six independent variables such as high quality and perfection, the brand loyalty, the consistency in price and quality, the attraction of body, health care, the concept of self-image and the effects of skin aging. By employing quantitative research method with 243 women consumers, the findings show that five factors that influence the purchasing intention of Korean cosmetics including the brand loyalty, the consistency in price and quality, the attraction of body, health care, the concept of self-image and the effects of skin aging. In which, the most powerful factor is the effects of skin aging. In particular, the factor of high quality and perfection has no effect on the Korean cosmetics consumption behavior of women in Ho Chi Minh City. Also, the study proposes several implications for domestic and foreign cosmetics manufactuers to improve their marketing policies.

Bài nghiên cứu mục đích nhằm khám phá các nhân tố ảnh hưởng đến quyết định mua mỹ phẩm Hàn Quốc của nữ giới tại địa bàn Thành phố Hồ Chí Minh. Mô hình nghiên cứu đề xuất bao gồm sáu biến độc lập: Coi trọng chất lượng cao, tính hoàn hảo; Coi trọng nhãn hiệu, giá ngang với chất lượng; Sự hấp dẫn của cơ thể; Quan tâm đến sức khỏe; Quan niệm về hình ảnh bản thân; Ảnh hưởng của lão hóa da. Với mẫu khảo sát là 243 người tiêu dùng, kết hợp phương pháp nghiên cứu định lượng, kết quả cho thấy năm nhân tố có tác động đến quyết định mua của người tiêu dùng bao gồm: Coi trọng nhãn hiệu, giá ngang với chất lượng; Sự hấp dẫn của cơ thể; Quan tâm đến sức khỏe; Quan niệm về hình ảnh bản thân; Ảnh hưởng của lão hóa da, trong đó, Ảnh hưởng của lão hóa da mạnh nhất. Đặc biệt, nhân tố Coi trọng chất lượng cao, tính hoàn hảo không có tác động đến quyết định mua mỹ phẩm Hàn Quốc của nữ giới tại địa bàn Thành phố Hồ Chí Minh. Bài nghiên cứu đề xuất một số kiến nghị để các doanh nghiệp trong và ngoài nước hoàn thiện chính sách marketing trong thời gian tới để thúc đẩy ý định mua của người tiêu dùng.

In 2019, Vietnam Dairy Products Joint Stock Company (Vinamilk) accounted for 50% proportion of milk production throughout the country and more than 50% of the total revenue of the dairy industry, constantly affirming its position as the leading dairy company in Vietnam (Vinamilk, 2019). According to the achievements of Vinamilk as mentioned above, the author would like to discover its strength as well as what factors affecting the consumption decisions of customers in HCMC when choosing dairy products, therefrom, suggesting solutions and recommendations so as to contribute to improve products quality and services of Vinamilk. Besides, the author also would like to advise recommendations for Korean dairy companies whilst penetrating Vietnam’s dairy market. The research analyses 5 factors influencing dairy products consumption decisions in HCMC, and the result presents that the main 5 consumption determinants are product quality, price, uses, brand and packaging, and information influence. Among those, the most influential factor to consumption decisions is product quality. Market researchers usually misunderstand that advertising would help the products easily sold; however, as the matter of fact, Vietnamese consumers concern more of product quality over brand advertising and packaging. By rsearching factors that influence consumers’ decisions of dairy products, in the case of Vinamilk, there are 2 limitations that are still remaining. Firstly, the author's survey is proceeded only at HCMC market, specifically in urban areas, which is the highly economic-developed and highly need of dairy products market. The author has not expanded the survey of Vinamilk's dairy consumption in rural areas or compared the differences in consumption needs of regions such as the North, the Central, ... of Viet Nam. Secondly, according to analysis results, the adjusted R2 is 0.589, which indicates that 58.9% of the variation in purchasing decisions is explained by the variation of 5 independent variables, while 41.1% of the variation in purchasing decisions is made due to other factors that have not yet been found. Therefore, this is a research gap for the author to continuously research next time.

Năm 2019, Công ty cổ phần sữa Việt Nam (Vinamilk) chiếm 50% tỷ trọng về sản lượng sữa cả nước và hơn 50% tổng doanh thu ngành sữa, tiếp tục khẳng định vị trí công ty sữa hàng đầu của Việt Nam Với mạng lưới tiêu dùng rộng khắp, đặc biệt là ở khu vực thành thị, có gần 92% người tiêu dùng chọn mua sản phẩm Vinamilk, cao hơn nhiều so với các thương hiệu khác (Vinamilk, 2019). Bài viết đã phân tích 5 nhân tố ảnh hưởng đến quyết định tiêu dùng sản phẩm sữa tại thị trường HCMC để tìm hiểu sức mạnh của Vinamilk tại thị trường này và những nhân tố chính ảnh hưởng đến quyết định lựa chọn mua sản phẩm của Vinamilk. Kết quả phân tích cho thấy có 5 nhân tố ảnh hưởng chính đến quyết định tiêu dùng này đó là chất lượng sản phẩm, giá cả sản phẩm, công dụng sản phẩm,thương hiệu và bao bì sản phẩm và ảnh hưởng của thông tin. Trong đó, nhân tố tác động mạnh nhất đến quyết định tiêu dùng là chất lượng sản phẩm. Trên cơ sở đó, tác giả muốn giúp các công ty sữa Hàn Quốc có các chiến lược phù hợp khi đưa sữa vào tiêu thụ tại thị trường Việt Nam.

This study deals with the taxi supply models based on the urban characteristics. The objective is to develop the appropriate taxi supply model to the urban characteristics by type, after classifying Korea cities and counties with the various urban indices. The study gives particular attentions to the multiple regression analyses and neural network models. Five significant factors are induced, through the factor analysis based on the 18 urban indices. All cities and counties are classified by 4 groups according to the cluster analysis. F-tests indicate that the differences of taxi supply size exist among urban groups classified by factor 1, 2 and 3. Multiple regression analyses show that the models by urban groups classified by the factor 1 are well fitted to the given data. Also the neural network models are better than multiple regression models in the view of fitness to the given data.

10 GHz 이상의 주파수에서는 강우가 위성링크감쇠의 유력한 전달현상이다. 강우감쇠를 예측하기 위해서는 평균년의 0.01%를 초과하는 1분단위로 누적된 강우율이 필요하다. 대부분의 강우데이터는 60분 누적시간으로 측정되었기 때문에, 강우데이터를 다수의 누적시간으로부터 1분 누적시간으로 변환하는 연구가 많이 수행되었다. 이 논문은 새로운 변환 모델인 다중회기모형을 제안하며, 제안방안은 기존 방안보다 우수한 성능을 보여 주었다.

At frequencies above 10 GHz, rain is a dominant propagation phenomenon on satellite link attenuation. The prediction ofrain attenuation is based on the point rainfall rate for 0.01 % of an average year with one minute integration time. Mostof available rain data have been measured with 60 minutes integration time, and many researchers have been studying onconverting the rainfall rate data from various integration times to one minute integration time. This paper proposes a newMultiple Regression model for the conversion, and the proposed schemes show better performance than the existing schemes.

This study aims to develop a regression model using data from the Ammunition Stockpile Reliability Program (ASRP) to predict the shelf life of 81mm mortar high-explosive shells. Ammunition is a single-use item that is discarded after use, and its quality is managed through sampling inspections. In particular, shelf life is closely related to the performance of the propellant. This research seeks to predict the shelf life of ammunition using a regression model. The experiment was conducted using 107 ASRP data points. The dependent variable was 'Storage Period', while the independent variables were 'Mean Ammunition Velocity,' 'Standard Deviation of Mean Ammunition Velocity,' and 'Stabilizer'. The explanatory power of the regression model was an R-squared value of 0.662. The results indicated that it takes approximately 55 years for the storage grade to change from A to C and about 62 years to change from C to D. The proposed model enhances the reliability of ammunition management, prevents unnecessary disposal, and contributes to the efficient use of defense resources. However, the model's explanatory power is somewhat limited due to the small dataset. Future research is expected to improve the model with additional data collection. Expanding the research to other types of ammunition may further aid in improving the military's ammunition management system.

The purpose of this paper is to establish a method estimating the daily urban water demand using statistical analysis that is used for developing the efficient management and operation of the water supply facilities, and accurary of the model is verified by error rate and F-value. The data used in this study were the daily urban water use, the weather conditions such as temperature, precipitation, relative humidity, etc, and the day of The week. The case study was taken placed for the city of Namwon in Korea. The raw data used in this study were rearranged either by month or by season for analysis purpose, and the statistical analysis was applied to the data to obtain the regression model As a result of this study, the linear regression model was developed to estimate the daily urban water use with weather condition. The regression constant and coefficients of the model were determined for each month of a year. The accuracy of the model was within 3% of average error and within 11% of maximum error. The resulting model was found to he useful to the practical operation and management of the water supply facilities.

This study is to estimate the spatial soil moisture using multiple linear regression model (MLRM) and 15 minutes interval Land Surface Temperature (LST) data of Communication, Ocean and Meteorological Satellite (COMS). For the modeling, the input data of COMS LST, Terra MODIS Normalized Difference Vegetation Index (NDVI), daily rainfall and sunshine hour were considered and prepared. Using the observed soil moisture data at 9 stations of Automated Agriculture Observing System (AAOS) from January 2013 to May 2015, the MLRMs were developed by twelve scenarios of input components combination. The model results showed that the correlation between observed and modelled soil moisture increased when using antecedent rainfalls before the soil moisture simulation day. In addition, the correlation increased more when the model coefficients were evaluated by seasonal base. This was from the reverse correlation between MODIS NDVI and soil moisture in spring and autumn season.

정확한 토양수분 관측은 수문 과정 이해와 기후 변화에 따른 재해 대응에 필수적이다. 그러나 한반도 지역은 복잡한 지형과 식생 조건으로 인해 기존 위성 기반 토양수분 산출의 정확도에 한계가 존재한다. 본 연구는 2020년 1월 부터 2025년 6월 까지 기간동안 원격탐사 자료를 활용한 다중선형회귀(Multiple Linear Regression, MLR) 모형을 구축하여 고해상도 토양수분 자료를 생산하는 것을 목표로 수행되었다. 주요 입력 변수로 Sentinel-1A 위성의 VV 편파 후방산란계수, 기상청 합성 레이더 기반 1일,2일,3일 누적 강우량, 그리고 연속 무강우일수를 활용하였다. 모형은 농촌진흥청(RDA)의 충청권 13개 지점 토양수분 관측자료를 이용해 검증하였으며, 높은 성능(MAE: 2.49~10.3, R: 0.517~0.768)을 나타내었다. 또한 토지피복 유형별 분석에서도 모형은 지표 특성과 강우 패턴에 따른 토양수분 변화를 안정적으로 재현하였다. 이는 원격 탐측 자료가 지상관측 토양수분 자료를 효과적으로 보완, 대체 할 수 있음을 의미하며, 개발된 모형은 가뭄 모니터링, 농업 생산성 평가, 산사태 및 수문재해 예측 등 다양한 분야의 기초자료로 활용될 수 있을 것으로 기대된다.

Accurate soil moisture observation is essential for understanding hydrological processes and supporting disaster response under climate change. However, in South Korea, the complex topography and vegetation conditions limit the accuracy of satellite-based soil moisture estimation. This study, conducted from January 2020 to June 2025, aimed to produce high resolution soil moisture data using a multiple linear regression (MLR) model based on remote sensing data. Key input variables included the VV-polarized backscattering coefficient from Sentinel-1A, cumulative 1, 2, and 3day precipitation derived from Korea Meteorological Administration (KMA) radar data, and consecutive dry days. The model was validated against in situ soil moisture observations at 13 stations in the Chungcheong region provided by the Rural Development Administration (RDA), showing high performance (MAE: 2.49~10.3, R: 0.517~0.768). The model also highly reproduced soil moisture variations across different land cover types, reflecting the influence of both surface conditions and precipitation patterns. These results demonstrate that remotely sensed based data can effectively complement ground observations. The developed model provides a reliable foundation for various applications, including drought monitoring, agricultural productivity assessment, landslide prediction, and flood hazard evaluation.

본 연구의 목적은 기상자료(강수량, 최고기온, 최저기온, 평균기온, 평균풍속) 기반의 다중선형 회귀모형을 개발하여 농업용저수지 저수율을 예측하는 것이다. 나이브 베이즈 분류를 활용하여 전국 1,559개의 저수지를 지리형태학적 제원(유효저수량, 수혜면적, 유역면적, 위도, 경도 및 한발빈도)을 기준으로 30개 군집으로 분류하였다. 각 군집별로, 기상청 기상자료와 한국농어촌공사 저수지 저수율의 13년(2002~2014) 자료를 활용하여 월별 회귀모형을 유도하였다. 저수율의 회귀모형은 결정계수($R^2$)가 0.76, Nash-Sutcliffe efficiency (NSE)가 0.73, 평균제곱근오차가 8.33%로 나타났다. 회귀모형은 2년(2015~2016) 기간의 기상청 3개월 기상전망자료인 GloSea5 (GS5)를 사용하여 평가되었다. 현재저수율과 평년저수율에 의해 산정되는 저수지 가뭄지수(Reservoir Drought Index, RDI)에 의한 ROC (Receiver Operating Characteristics) 분석의 적중률은 관측값을 이용한 회귀식에서 0.80과 GS5를 이용한 회귀식에서 0.73으로 나타났다. 본 연구의 결과를 이용해 미래 저수율을 전망하여 안정적인 미래 농업용수 공급에 대한 의사결정 자료로 사용할 수 있을 것이다.

The purpose of this study is to predict monthly agricultural reservoir storage by developing weather data-based Multiple Linear Regression Model (MLRM) with precipitation, maximum temperature, minimum temperature, average temperature, and average wind speed. Using Naïve-Bayes classification, total 1,559 nationwide reservoirs were classified into 30 clusters based on geomorphological specification (effective storage volume, irrigation area, watershed area, latitude, longitude and frequency of drought). For each cluster, the monthly MLRM was derived using 13 years (2002~2014) meteorological data by KMA (Korea Meteorological Administration) and reservoir storage rate data by KRC (Korea Rural Community). The MLRM for reservoir storage rate showed the determination coefficient ($R^2$) of 0.76, Nash-Sutcliffe efficiency (NSE) of 0.73, and root mean square error (RMSE) of 8.33% respectively. The MLRM was evaluated for 2 years (2015~2016) using 3 months weather forecast data of GloSea5 (GS5) by KMA. The Reservoir Drought Index (RDI) that was represented by present and normal year reservoir storage rate showed that the ROC (Receiver Operating Characteristics) average hit rate was 0.80 using observed data and 0.73 using GS5 data in the MLRM. Using the results of this study, future reservoir storage rates can be predicted and used as decision-making data on stable future agricultural water supply.

This paper is an empirical study on the impact of organizational culture upon employee satisfaction based on the survey data of a logistics company. Robert Levering's organizational culture model, which categorizes cultural aspects into five major areas( credibility, respect, fairness, pride, and fun), was adopted as the basis for nominating independent variables of the multiple regression analysis model. 803 data sets were collected from the questionnaire survey. Statistical test of hypothesis upon the multiple regression model indicated that 'respect', 'fairness', and 'pride' play significant role on employee satisfaction. Whereas the remaining two cultural aspects, i.e., 'credibility' and 'fun,' do not play any significant role. However, care must be given so that this result might not be generalized for other organizations in the defense industry.

본 연구는 중도입국청소년이 경험하는 문화적응스트레스에 영향을 미치는 환 경적 요인을 살펴보고, Bronfenbrenner의 생태체계모델 관점을 적용하여 위계 적다중회귀분석을 통해 분석하여 유의미한 사회복지적 실천적 함의와 정책적 함 의를 도출하기 위한 연구이다. 경기도에 거주하거나 학교를 다니는 10~20세 사 이의 279명의 중도입국청소년의 설문조사 자료를 활용하여 분석하였다. 주요 결과는 다음과 같다. 첫째, Bronfenbrenner의 생태체계모델은 중도입국 청소년의 미시체계에서의 환경적 요인분석에 적용가능성을 보였다. 둘째, 문화 적응스트레스에 영향을 미치는 요인을 연구한 본 연구의 연구모형은 위계적회귀 모델에서 개인요인 가족요인 학교요인을 모두 넣은 모델3이 가장 큰 설명력을 보였다. 셋째, 가족요인에서 부모의 지지가, 학교요인에서는 학교소속감이 문화 적응스트레스를 감소시키는 것으로 나타났다. 이번 연구에서는 데이터의 한계로 미시체계의 환경적 변인만을 살펴보았기에, 추후에 중간체계 외부체계 거시체계의 환경적 변인이 문화적응스트레스에 미치 는 영향에 대한 추가 연구가 필요하다.

This study examines environmental factors that affect the cultural adjustment stress experienced by adolescent immigrants. By applying Bronfenbrenner’s ecological system model, this study analyzes the environmental factors through the hierarchical multiple regression analysis. It Is a study to derive meaningful social and practical implication and suggest its results in the policy level. It was analyzed by using the survey data of 279 adolescent immigrants between the age of 10 to 20 who lived or attended schools in Gyeonggi-do Province. The main results are as follows. First, Bronfenbrenner’s ecological system model showed its potential applicability on analyzing environmental factors of adolescent immigrants. Second, the research model of this study, which examined factors affecting the cultural adjustment stress, showed that the model 3, which had all of the individual, family, and school factors, showed the maximum explanation in the hierarchical regression analysis. Third, parents’ support of family factors and sense of school belonging of school factors decreased the cultural adjustment stress. In this study, only the environmental variables in microsystem level was tested because of the limitation of data. Further research is needed to examine the impact of environmental variables affecting the cultural adjustment stress on mesosystem, exosystem, and macrosystem levels afterwards.

본 연구는 중도입국청소년이 경험하는 문화적응스트레스에 영향을 미치는 환 경적 요인을 살펴보고, Bronfenbrenner의 생태체계모델 관점을 적용하여 위계 적다중회귀분석을 통해 분석하여 유의미한 사회복지적 실천적 함의와 정책적 함 의를 도출하기 위한 연구이다. 경기도에 거주하거나 학교를 다니는 10~20세 사 이의 279명의 중도입국청소년의 설문조사 자료를 활용하여 분석하였다. 주요 결과는 다음과 같다. 첫째, Bronfenbrenner의 생태체계모델은 중도입국 청소년의 미시체계에서의 환경적 요인분석에 적용가능성을 보였다. 둘째, 문화 적응스트레스에 영향을 미치는 요인을 연구한 본 연구의 연구모형은 위계적회귀 모델에서 개인요인 가족요인 학교요인을 모두 넣은 모델3이 가장 큰 설명력을 보였다. 셋째, 가족요인에서 부모의 지지가, 학교요인에서는 학교소속감이 문화 적응스트레스를 감소시키는 것으로 나타났다. 이번 연구에서는 데이터의 한계로 미시체계의 환경적 변인만을 살펴보았기에, 추후에 중간체계 외부체계 거시체계의 환경적 변인이 문화적응스트레스에 미치는 영향에 대한 추가 연구가 필요하다.

This study examines environmental factors that affect the cultural adjustment stress experienced by adolescent immigrants. By applying Bronfenbrenner’s ecological system model, this study analyzes the environmental factors through the hierarchical multiple regression analysis. It Is a study to derive meaningful social and practical implication and suggest its results in the policy level. It was analyzed by using the survey data of 279 adolescent immigrants between the age of 10 to 20 who lived or attended schools in Gyeonggi-do Province. The main results are as follows. First, Bronfenbrenner’s ecological system model showed its potential applicability on analyzing environmental factors of adolescent immigrants. Second, the research model of this study, which examined factors affecting the cultural adjustment stress, showed that the model 3, which had all of the individual, family, and school factors, showed the maximum explanation in the hierarchical regression analysis. Third, parents’ support of family factors and sense of school belonging of school factors decreased the cultural adjustment stress. In this study, only the environmental variables in microsystem level was tested because of the limitation of data. Further research is needed to examine the impact of environmental variables affecting the cultural adjustment stress on mesosystem, exosystem, and macrosystem levels afterwards.

An observation of the mortality rate in the construction industry by age for the past five years shows that the mortality rate of those 50 years of age and older is consistently increasing. As the number of middle and old aged workers is continuously increasing, their hazard rate is also increasing so hazard prevention efforts for them are being urgently demanded. In particular, with regard to middle and old aged workers, there are many experienced in construction so wrong work methods or habits adopted in the past can cause hazards so safety education that can allow middle and old aged workers to adopt proper work methods or habits is urgently needed. At this, this study proposes a safety education model for middle and old aged workers that takes into consideration the characteristics of middle and old aged workers. The accuracy of the developed model was evaluated and its applicability was reviewed based on data. Verification of this proposed model was made by conducting a likelihood ratio test and correction rate test and likelihood ratio test results showed that all safety education elements were significant at a significance level of 0.1% and they also had a goodness of fit of 83%, so their potential for applicability in actual construction sites was verified. The accuracy of this model will increase as more data accumulates in the future.

Forecasting quality of complex products is a major concern for quality engineers and enterprises' decision-makers. But only a few researchers have investigated how multiple linear regression analysis can improve forecasts. This article presents a predictive control model for personalized forecasting system of complex product quality using multiple linear regression analysis. First, this paper compares the performance of personalized forecasting system for product quality which leads to a better understanding of the applicability of the forecasting model. Second, this paper identifies the effect of predictive control model of multiple linear regression analysis on forecasting accuracy. Then, an example shows the feasibility of using predictive control model in complex product quality personalized forecasting. Finally, it makes a conclusion that the approved prediction system embraces superiority over the prediction of the quality factors and manufacturing resources.

In regression model with nested error structure interval estimations on regression coefficients in different stages are proposed. Ordinary least square estimators and generalized least square estimators of the regression coefficients in this model are derived for between and within group model. The confidence intervals are dervied by using independent idstributional properties between regression coefficient estimators and quadratic froms obtained from the model.

The objective of the current study was to characterize and apply multiple regression model relating to vegetation values of the plant species over salt marshes. For each salt marsh community, vegetation and soil variables were investigated in the western coast and the southern coast in South Korea. Osmotic potential of soil and $Cl^-$ content of soil as independent variable had positive and negative influences on vegetation values. Multiple regression model showed that vegetation values of 14 coastal plant communities were determined by pH of soil, osmotic potential of soil and sand content. The multiple regression equation may be applied to the explanation of distribution and abundance of plant communities with exiting ordination plots.

This paper focuses on the developed empirical models for the prediction on top-bead width in GMA(Gas Metal Arc) welding process. Three empirical models have been developed: linear, curvilinear and an intelligent model. Regression analysis was employed fur optimization of the coefficients of linear and curvilinear model, while Genetic Algorithm(GA) was utilized to estimate the coefficients of intelligent model. Not only the fitting of these models were checked, but also the prediction on top-bead width was carried out. ANOVA analysis and contour plots were respectively employed to represent main and interaction effects between process parameters on top-bead width.

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

In the present paper, the fiber theory has been employed to model the reinforced concrete (RC) deep beams (DBs) considering the reinforcing steel bar-concrete interaction. To simulate numerically the behavior of materials, the uniaxial materials' constitutive laws have been employed for reinforcements and concrete and the bond stress-slip between the reinforcing steel bars and surrounding concrete are taken into account. Because of the high sensitivity of DBs to shear deformations, the Timoshenko beam theory has been applied. The shear stress-strain (S-SS) relationship has been defined by the modified compression field theory (MCFT) model. By modeling about 300 RC panels and employing a produced numerical database, a study has been carried out to show the sensitivity of the MCFT model. This is performed based on the multiple linear regression (MLR) models. The results of this research also illustrate how different parameters such as characteristic compressive strength of concrete, yield strength of reinforcements and the percentages of reinforcements in different directions get involved in the shear behavior of RC panels without applying complex theories. Based on the results obtained from the analysis of the MCFT S-SS model, a relatively simplified numerical S-SS model has been proposed. Application of the proposed S-SS model in modeling and analyzing the considered samples indicates that there is a good agreement between the simulated and the experimental test results. The comparison between the proposed S-SS model and the MCFT model indicates that in addition to the advantage of better accuracy, the main advantage of the proposed method is simplicity in application.