• Journal of Korean Society for Atmospheric Environment
• /
• v.30 no.3
• /
• pp.281-290
• /
• 2014

This study was conducted to evaluate the distribution characteristics of $PM_{10}$ and heavy metals concentrations in the ambient air of Gyeonggi-do area by region and season from February, 2013 to March, 2014. The regression model for the prediction of formation characteristics and contamination degree of $PM_{10}$ and heavy metals by correlation analysis and regression analysis for using the multivariate statistical analysis was also established. The main wind direction during the investigation period was South East (SE) and West South West (WSW) winds, and the concentration of $SO_2$ at Ansan with industrial region showed 1.6 times higher than Suwon, Euiwang with residential region. The concentrations (median) of Pb, Cu and Ni at Ansan showed 3.2~4.5, 1.9~2.2 and 1.7~2.6 times respectively higher than those at Suwon. By the seasonal concentration variation, the concentrations of $PM_{10}$, Pb, Fe and As in winter and spring (December to May) showed 1.7, 1.9, 1.9 and 2.7 times respectively higher than those in summer and fall (June to November). As, Fe and $PM_{10}$ had a big difference by the seasonal factors, and Cu and Ni were evaluated to be influenced by the regional factors. From the results of correlation analysis among the target items, the correlation coefficient of PM and Mn had 0.82 (p/0.01) and that of Fe and Mn had 0.82 (p/0.01), which showed high correlation. And the correlation coefficients for $SO_2$ and Pb, CO and $PM_{10}$ were 0.66 (p/0.01) and 0.62 (p/0.01) respectively. The multiple linear regression models for $PM_{10}$, Pb, Cu, Cr, As, Ni, Fe and Mn were established by independent variables of CO, $SO_2$ and meteorological factors (wind speed, relative humidity). In the regression models, independent variable $SO_2$ was in cause-and-effect relationship with all dependent variables, and $PM_{10}$, Fe and Mn were influenced by CO and wind speed, and Pb, Cu, Ni and As had a main factor of $SO_2$.

• Journal of Environmental Health Sciences
• /
• v.32 no.4 s.91
• /
• pp.324-335
• /
• 2006

Five sampling sites were selected to investigate the distribution characteristics of air pollutants with pollution sources and weather conditions in Ulsan. $SO_2,\;NO_2,\;O_3,\;CO,\;PM_{10}$ concentrations and weather conditions with time were analyzed by using several statistical methods. Also, the distribution characteristics of ambient air quality were estimated by pollution-rose and multi-regression analysis. As a result of the analysis, $NO_2,\;CO\;and\;PM_{10}$ concentrations were high in winter season, whereas $SO_2\;and\;O_3$ concentrations were high in summer season. This concentration distribution was caused by the unfavorable geographical location, which the residential area was located at the downwind direction to industrial area. From the pollution-rose, we confirmed that each pollutant influenced the downwind residential areas because of seasonal wind direction. $SO_2$ concentration has shown positive correlation of $0.2{\simm}0.3$ for $NO_2,\;PM_{10}$ and temperature, while $O_3$ concentration has shown negative correlation. Also, $NO_2$ and CO concentrations, pollutants generated by combustion, have shown positive correlation, while $O_3$ concentration and temperature have shown negative correlation. Therefore, it could be suggested that a seasonal air quality policy and a new guideline of air quality was necessary in each season with wind directions to reduce the air pollution level in Ulsan.

• Journal of Ecology and Environment
• /
• v.47 no.3
• /
• pp.157-167
• /
• 2023

Background: The most hazardous wastewater sources in the northern part of the Middle Nile Delta, Egypt; receiving a massive amount of agricultural, industrial, and sewage drainage are Kitchener drain which is one of the tallest drainage systems, and Burullus Lake which represents the 2nd largest Egyptian coastal lake. Results: The current work is to determine the abundance and frequency of cyanophytes, chlorophytes, and bacillariophytes and the correlation between them and environmental abiotic components. Among sixty nine microalgal species, 19 species are belong Cyanophyta, 26 belong Chlorophyta and 24 belong Bacillariophyta. Genus Scenedesmus (Chlorophyta) was the most abundant in the study area (13 species), followed by Genus Oscillatoria (9 species) and Genus Navicula (7 species). Nostoc muscorum and Chlorella vulgaris were the most common and recorded in all sites (100% of the locations) under study. The application of the two-way indicator species analysis (TWINSPAN) and detrended correspondence analysis revealed agglomerating of 4 groups (communities) at 4th level of classification and reasonable segregation between these groups. Zinc, cadmium and lead were showed the highest levels (0.26±0.03, 0.26±0.06, and 0.17±0.01 ppm, respectively). Conclusions: The correlation analysis between water and community variables indicated a high negative correlation of total algae richness with nickel (r = -0.936, p < 0.01). Cyanophyta and Bacillariophyta were correlated negatively (r = -0.842, p < 0.01). However, Chlorophyta showed a negative richness with each of Ni and Pb (r = -0.965, -0.873, respectively) on one hand and a high positive correlation was revealed (r = 0.964) with all environmental variables on the other hand.

• Journal of Korean Society of Occupational and Environmental Hygiene
• /
• v.23 no.2
• /
• pp.65-74
• /
• 2013

Objectives: This study attempted to develop a method to measure ultra-trace lead concentrations in plasma using Inductively Coupled Plasma Mass Spectrometry(ICP-MS) and to test whether plasma lead can be used as a biomarker for the biological monitoring of exposure to lead. Methods: Lead concentrations in 160 plasma samples of field workers and 42 plasma samples from the control group were measured by ICP-MS. Blood zinc protophorphyrin(ZPP) concentrations and urinary ${\delta}$-aminolevulinic acid${\delta}-ALA$) were measured for correlation analysis with plasma lead. Results: The mean lead level in the plasma of the workers exposed to lead at work were 786.1 ng/L. Plasma lead levels were not correlated with blood ZPP or urinary ${\delta}-ALA$ concentrations. Otherwise, plasma lead levels showed a good correlation coefficient of 0.400 with blood lead levels, and their correlation coefficient had a better value of 0.552 for the non-smoking and drinking group. In the general population group which was not exposed to lead in the workplace and was considered the control group, the mean concentration of plasma lead was 123.1 ng/L. The plasma lead levels for the general population group showed a good correlation coefficient of 0.520 with blood ZPP and urinary ${\delta}-ALA$ concentrations.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.7
• /
• pp.4459-4466
• /
• 2015

This study was conducted to identify the factors that influence nursing students' adjustment to college life. Self-report questionnaire surveys were conducted toward 333 South Korean nursing students to measure emotional intelligence, perfectionistic self-presentation, and clinical stress. Data were collected from October 20th to December 8th, 2014. The collected data were analyzed by using t-test, one way ANOVA, Pearson correlation coefficient, and multiple linear regression analysis. The results indicated that emotional intelligence, religion, perfectionistic self-presentation significantly influenced on the adjustment to college life and the explanatory power of the model was 27.2%. Based on the study results, we suggest that it is necessary to develop and apply the educational strategy to improve the emotional intelligence for the adjustment to college life.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.9 no.6
• /
• pp.1811-1817
• /
• 2008

According to development of electronic commerce, the university's curriculums related electronic commerce should be renewed and added some chapters. I researched on university curriculums of electronic commerce, which has required by enterprise. In particular, the electronic commerce consist of two parts; IT technology and business management. Therefore it is regarded as a convergence subject. I did a correlation analysis between management's performance of enterprise and importance of university curriculum on electronic commerce. The questionnaire includes demands of enterprise and e-business model on electronic commerce. The result of this research indicates a necessary of changing by correlation ratio. Based on this research result, I suggest extended curriculums for electronic commerce.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.12 no.11
• /
• pp.4764-4769
• /
• 2011

This study intends to investigate the organizational commitment and Its related factors among medium hospital of nurses. The collected data were analyzed descriptive statistics, t-test, ANOVA, Scheffe's test, Pearson correlation coefficient and stepwise multiple regression using SPSS 19.0 Program. The score of level of organizational commitment was statistically significant difference according to working period, marital state, monthly income, personality, night-duty. The score of organizational commitment level correlated positively with job satisfaction and burnout. Stepwise multiple regression analysis for organizational commitment level revealed that the most powerful predictor was burnout, job satisfaction and night-duty explained 49.5% of the variance. Therefore, It suggested that goal of increasing nurses' organizational commitment in hospital should be helped them raise job satisfaction and decrease nurses' burnout and night duty.

• The Journal of the Korea Contents Association
• /
• v.20 no.7
• /
• pp.66-85
• /
• 2020

The purpose of this study is to explain and predict trends the AI development process based on AI technology patents (total) and AI reporting frames in major newspapers. To that end, a summary of South Korean and U.S. technology patents filed over the past nine years and the AI (Artificial Intelligence) news text of major domestic newspapers were analyzed. In this study, Topic Modeling and Time Series Return Analysis using Big Data were used, and additional network agenda correlation and regression analysis techniques were used. First, the results of this study were confirmed in the order of artificial intelligence and algorithm 5G (hot AI technology) in the AI technical patent summary, and in the news report, AI industrial application and data analysis market application were confirmed in the order, indicating the trend of reporting on AI's social culture. Second, as a result of the time series regression analysis, the social and cultural use of AI and the start of industrial application were derived from the rising trend topics. The downward trend was centered on system and hardware technology. Third, QAP analysis using correlation and regression relationship showed a high correlation between AI technology patents and news reporting frames. Through this, AI technology patents and news reporting frames have tended to be socially constructed by the determinants of media discourse in AI development.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.41 no.1
• /
• pp.84-93
• /
• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.3
• /
• pp.76-82
• /
• 2017

Recently, the material industry in the world has started appreciating the value of new materials that can overcome the limitation of steel material. In particular, new materials are expected to play a very important role in the future industry, demonstrating superior performance compared to steel in lightweight materials and ability to maintain in high temperature environments. Carbon materials have recently increased in value due to excellent physical properties such as high strength and ultra lightweight compared to steel. However, they have not overcome the limitation of productivity and price. The carbon materials are classified into various composites depending on the purpose of use and the performance required. Typical composites include carbon-glass, carbon-carbon, and carbon-plastic composites. Among them, carbon-carbon composite technology is a necessary technology in aviation and space, and can be manufactured with high investment cost and technology. In this paper, in order to find the optimal conditions to achieve productivity improvement and cost reduction of carbon material densification process, the correlation between each process parameters and results of densification is first analyzed. The main process parameters of the densification process are selected by analyzing the correlation results. And then a certain linear relationship between major process variables and density of carbon materials is derived by performing a regression analysis based on the historical production result data. Using the derived casualty, the optimal management range of major process variables is suggested. Effective process operation through optimal management of variables will have a great effect on productivity improvement and manufacturing cost reduction by shortening the lead time.