• International Journal of Computer Science & Network Security
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• 제23권9호
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• pp.37-46
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• 2023

The main aim of the article is to solve the problem of automating price monitoring using marketing forecasting methods and Excel functionality under martial law. The study used the method of algorithms, trend analysis, correlation and regression analysis, ANOVA, extrapolation, index method, etc. The importance of monitoring consumer price developments in market pricing at the macro and micro levels is proved. The introduction of a Dummy variable to account for the influence of martial law in market pricing is proposed, both in linear multiple regression modelling and in forecasting the components of the Consumer Price Index. Experimentally, the high reliability of forecasting based on a five-factor linear regression model with a Dummy variable was proved in comparison with a linear trend equation and a four-factor linear regression model. Pessimistic, realistic and optimistic scenarios were developed for forecasting the Consumer Price Index for the situation of the end of the Russian-Ukrainian war until the end of 2023 and separately until the end of 2024.

• KSCE Journal of Civil and Environmental Engineering Research
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• 제28권2D호
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• pp.155-162
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• 2008

This study shows how to estimate the design hour factor when the counting stations don't have all of the hourly volumes such as in a coverage survey. A coverage survey records traffic volume from 1 to 5 times in a year so it lacks the detailed information to calculate the design hour factor. This study used the traffic volumes of permanent surveys to estimate the design hour factor in coverage surveys using correlation and regression analysis. A total 7 independent variables are used : the coefficient of variance of hourly volume, standard deviation of hourly volume, peak hour volume, AADT, heavy traffic volume proprotion, day time traffic volume proportion and D factor. All of variables are plotted on a curve, so it must use non-linear regression to analyze the data. As a result the coefficient of determination and MAE are good at logarith model using AADT.

• International Journal of Advanced Culture Technology
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• 제6권2호
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• pp.72-79
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• 2018

The purpose of this study was to analyze general characteristics and empathy factors of nursing student's major satisfaction. Participants in this study were 235 students from both located in J do and C do Universities. The research method was a cross-sectional survey and the survey period was from September 1 to 10, 2017. The questionnaire was used to investigate general characteristics, empathy, and major satisfaction. The analysis was based on frequency analysis, p value of t or F value, Pearson correlation, regression analysis, and hierarchical regression analysis using SPSS 18.0. The result of this study were as follows: (1) The C University showed higher satisfaction than J University(3.44), (2) the factors affecting major satisfaction were school location, grade, religion, cognitive empathy, and emotional empathy correlated, Regression analysis was used to examine factors that correlated with major satisfaction, followed by hierarchical regression analysis to identify the most influential factors. (3) The result of the analysis showed that the greatest influence factors on major satisfaction were the University location(${\beta}=.325$, p<.01), the cognitive empathy (${\beta}=.287$, p<.01), and the next order was negative grade(${\beta}=-.230$, p<.01). Based on the results of this stud, the following conclusions can be drawn. The most influential factor in the major satisfaction was the school location, but this was an irreversible factor. Therefore, if the cognitive empathy factor and grades are corrected, it can be said that it can increase the satisfaction of major in nursing University students. In this study, it was emphasized that cognitive empathy contained in the course of nursing education program and suggested guidance on major satisfaction in lower grades.

• International Journal of Highway Engineering
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• 제14권6호
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• pp.121-129
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• 2012

PURPOSES : Traffic accidents at intersections have been increased annually so that it is required to examine the causations to reduce the accidents. However, the current existing accident models were developed mainly with non-linear regression models such as Poisson methods. These non-linear regression methods lack to reveal complicated causations for traffic accidents, though they are right choices to study randomness and non-linearity of accidents. Therefore, to reveal the complicated causations of traffic accidents, this study used structural equation methods(SEM). METHODS : SEM used in this study is a statistical technique for estimating causal relations using a combination of statistical data and qualitative causal assumptions. SEM allow exploratory modeling, meaning they are suited to theory development. The method is tested against the obtained measurement data to determine how well the model fits the data. Among the strengths of SEM is the ability to construct latent variables: variables which are not measured directly, but are estimated in the model from several measured variables. This allows the modeler to explicitly capture the unreliability of measurement in the model, which allows the structural relations between latent variables to be accurately estimated. RESULTS : The study results showed that causal factors could be grouped into 3. Factor 1 includes traffic variables, and Factor 2 contains turning traffic variables. Factor 3 consists of other road element variables such as speed limits or signal cycles. CONCLUSIONS : Non-linear regression models can be used to develop accident predictions models. However, they lack to estimate causal factors, because they select only few significant variables to raise the accuracy of the model performance. Compared to the regressions, SEM has merits to estimate causal factors affecting accidents, because it allows the structural relations between latent variables. Therefore, this study used SEM to estimate causal factors affecting accident at urban signalized intersections.

• Journal of Environmental Science International
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• 제28권12호
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• pp.1101-1110
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• 2019

This study investigated Post-Occupancy Evaluation (POE) of cherry blossom trails 'Cherry Road' in Daegu Catholic Univ. campus, at Gyeonsan-city, Korea. The evaluation focused on image preference and satisfaction of users i.e., students, using questionnaire surveys. A total 201 questionnaire samples were analyzed and most of the respondents were in the age group of 20. Frequency analysis was conducted on demographics, use behavior, reliability, and means. Factor analysis and multiple regression analysis were conducted for user satisfaction and image preference. Over 80% of visitors came with companions during daytime. The most common motives for use were strolling and walking, event and meeting, passing. For user satisfaction the mean scores were highest for landscape beauty (4.22), image improvement (4.14), campus image (4.08). Night lighting facility received the lowest score (3.32). Factor analysis concerning user satisfaction was categorized into environment-human behavior and physical factors. Multiple regression analysis showed that the overall satisfaction of user was significantly influenced by five independent variables: 'harmonious' (β=.214), 'night lighting facility' (β=.173), 'landscape beauty' (β=.208), 'lawn care' (β=.154), and 'walking trails' (β=.123). The mean scores of image variables were highest for 'beautiful' (5.81), 'bright' (5.67), and 'open' (5.64). The lowest scores was for 'quiet' (4.47). Exploratory factor analysis led to three factors being categorized: aesthetics, comforts, and simplicity. Result of multiple regression analysis indicated that the preference of space image was significantly influenced by five variables: 'bright' (β=.397), 'refreshing' (β=.211), 'cool' (β=.219), 'clean' (β=.182), and 'natural' (β=.-142). Hence, Cherry Road has a high level of user satisfaction and image evaluation, which is interpreted as having various cultural events and value for students on campus. To improve the satisfaction of Cherry Road in the future, it is necessary to secure night lighting, to manage trash cans, and to secure rest space.

• Mass Spectrometry Letters
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• 제13권1호
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• pp.11-19
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• 2022

Although translational research is referred to clinical chemistry measures, correct weighting factors for linear and quadratic calibration curves with least-squares regression algorithm have not been carefully considered in bioanalytical assays yet. The objective of this study was to identify steroidogenic roles in preeclampsia and verify accuracy of quantitative results by comparing two different linear regression models with weighting factor of 1 and 1/x². A liquid chromatography-mass spectrometry (LC-MS)-based adrenal steroid assay was conducted to reveal metabolic signatures of preeclampsia in both serum and placenta samples obtained 15 preeclamptic patients and 17 age-matched control pregnant women (33.9 ± 4.2 vs. 32.8 ± 5.6 yr, respectively) at 34~36 gestational weeks. Percent biases in the unweighted model (w_i = 1) were inversely proportional to concentrations (-739.4 ~ 852.9%) while those of weighted regression (w_i = 1/x²) were < 18% for all variables. The optimized LC-MS combined with the weighted linear regression resulted in significantly increased maternal serum levels of pregnenolone, 21-deoxycortisol, and tetrahydrocortisone (P < 0.05 for all) in preeclampsia. Serum metabolic ratio of (tetrahydrocortisol + allo-tetrahydrocortisol) / tetrahydrocortisone indicating 11β-hydroxysteroid dehydrogenase type 2 was decreased (P < 0.005) in patients. In placenta, local concentrations of androstenedione were changed while its metabolic ratio to 17α-hydroxyprogesterone responsible for 17,20-lyase activity was significantly decreased in patients (P = 0.002). The current bioanalytical LC-MS assay with corrected weighting factor of 1/x² may provide reliable and accurate quantitative outcomes, suggesting altered steroidogenesis in preeclampsia patients at late gestational weeks in the third trimester.

• Journal of Distribution Science
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• 제13권6호
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• pp.97-104
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• 2015

Purpose - The distribution of agricultural products is changing due to recent shifts in environmental free trade. Specifically, the competitiveness of domestic agricultural products has weakened as a result of the Korea-China Financial Trade Agreement. Agricultural producers are faced with increasing difficulties and organized production centers are growing in importance daily. To overcome this crisis, agricultural producer organizations are vying for environment-friendly agricultural certifications, Good Agriculture Practices (GAP) and Hazard Analysis and Critical Control Point (HACCP). In particular, as consumer demand for higher safety grows, farmers are increasing their certification rates. Therefore, this certification system is expected to help strengthen the competitiveness of agricultural producer organizations. Research design/data/methodology - Organized production centers are classified by certification. A survey was conducted with 91 organizations using factor analysis and logistic regression analysis for the examination. The factor analysis results are as follows. Raw material procurement, education·specialization, marketing, joint business, organizing ability, business management, effectiveness, certification, and larger organizations were classified as the nine types of factors. These factors affect the organized production centers and are used in the logistic regression analysis. The purpose of such research and analysis is to suggest a direction for future production center policies. Results - The basic statistical results are as follows: analysis of the producer organizations of 91 sites, average number of members per site of 1,624, and average sales of 25,961 million won. Additionally, the average income per farmer is 175 million won, and the pooling system rate is 53.5%. The factor analysis results are as follows. Factor 1 consists of contract cultivation, ongoing shipment, selection subdivision, traceability, and major retailer management. Factor 2 consists of manual cultivation, specialty selection, education program, and R&D. Factor 3 consists of advertising, various dealers, various sales strategies, and a unified sales counter. Factor 4 consists of agricultural materials co-purchase, policy support, co-shipment, and incentives. Factor 5 consists of the co-selection and pooling system. Factor 6 consists of co-branding and operating by the organization's article. Factor 7 consists of the buy-sell ratio and rate of operation of the agriculture promotion center. Factor 8 consists of bargaining power in volume and participation rate of farmer certification. Factor 9 consists of increasing new subscribers. The logistic regression analysis results are as follows. Considering the results by type of certification, the environment-friendly agricultural certification type and the GAP certification type have a (+) influence. GAP and HACCP certification types affecting the education·specialization factor have a (+) influence. Considering the results for each type of certification, the environment-friendly agricultural certification types on the effectiveness factor have (-) influence; the HACCP certification types on the organizing ability and effectiveness factor have a (-) influence. Conclusions - Agricultural producer organizations should develop plans as follows: The organizations need to secure education for agricultural production; increase the pooling system ratio for sustainable organizational development; and, finally, expand the number of agricultural producer organizations.

• Journal of the Korean Society of Safety
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• 제35권2호
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• pp.55-60
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• 2020

The purpose of this study is to analyze the influence of the economic and workforce factors on the annual number of occupational accident fatalities occurring at the construction sites. The considered variables of the economic factor are the amount of economic losses caused by industrial accidents, the GDP, and the total monthly wage for each economically active population. And, the variables related to the workforce factor are the number of economically active population, the number of female economically active population, and the number of labor union members. The multiple regression analysis was conducted to determine the effect of two factors on the total number of occupational fatalities in the construction industry. The results show that GDP among considered variables in the economic factor had a statistically significant negative relationship with the number of the construction accident fatalities in the construction industry. Among variables related to the workforce factor, economically active population showed a statistically significant negative relationship with the number of the construction accident fatalities.

• Journal of Construction Engineering and Project Management
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• 제8권2호
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• pp.17-33
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• 2018

This study investigates the stability of professionals' cost variability factor-rankings across different levels of cost-variability and response scenarios. Descriptive statistics are used to examine the stability of factor-ranking for 20 cost variability factors and a Multinomial Logistic (MNL) regression model was implemented to examine the stability of cost variability factors across three cost variability levels. The finding on the descriptive statistics indicated that professionals' factors-rankings are stable only for external factors. The MNL regression results on factor-stability suggested that 8 out of the 20 evaluated factors were unstable determinant of lower cost variability levels. These factors are "risk associated with the project", "personal bias and poor professionalism of the estimators", "limited time available to complete the project", "lack of skills and experience by estimator" "geographical location of projects", "incomplete & rush designs for estimate", "unforeseen or unexpected site constraints", "high class bidders for the contractors". Similarly lack of experience and large size projects were observed to be unstable as well. These observations suggest that professionals' view on pre-tender cost variability factor-ranking yields unstable factor rankings hence should not be relied upon as the only mechanisms to mitigate cost related risks in construction projects.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• 제7권3호
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• pp.15-22
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• 2011

The chemistry factor and RTPTS margin for domestic reactor pressure vessel materials were analyzed by using the surveillance data which have been obtained from 8 nuclear power plants in Korea. The surveillance data have been used to assess the integrity of the pressure vessel under the pressurized thermal shock (PTS) event. The chemistry factor, which is determined by the Cu and Ni contents of vessel materials, is considered a proper tool to assess the $RT_{PTS}$. The chemistry factors, which were obtained from the surveillance data of domestic reactor pressure vessels, were investigated and compared with those of Regulatory Guide 1.99 in this study. Regressions for ${\Delta}RT_{NDT}$ were performed to expect the chemistry factor as a function of Cu and Ni, and to estimate $RT_{PTS}$ margin. The margin analysis was performed by comparing the regression graphs and standard deviations with those of Regulatory Guide 1.99. The standard deviations calculated by using the domestic surveillance data for base metal and welds are almost same as the standard deviations which are suggested on Regulatory Guide 1.99, Rev.2.