• Journal of agricultural medicine and community health
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• v.26 no.1
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• pp.37-56
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• 2001

This study was conducted to analyze the degree of changes in knowledge and attitude toward smoking and to examine the factors affecting knowledge and attitude for smoking after providing a smoking prevention program based on social influence model for a year to middle school students. Study population consists of 665 subjects of middle school students(aged 14 years) in Gumi city in Kyeongsangbukdo Province. Among them three-hundred sixty-seven students(intervention group) were educated to a smoking prevention program for 1 year from April 1999 to April 2000. School-based four-class program to prevent smoking was developed. The program provides instruction about short and long-term negative physiologic and social consequences of smoking and also discussed the health hazards of smoking, social pressure to smoke, peer norms regarding tobacco use, and refusal skill. A 45-item self-administered structured questionnaire was designed to evaluate the change of knowledge, attitude, smoking rate and the amount of smoking. The instrument was comprised of 11 knowledge items, thirteen attitude item and demographic items. Each scales were created by summing responses to each items within each scales and high scores on the knowledge, attitude, and smoking behavioral intention scales indicated positive responses. Based on the changes before and after the implementation of smoking prevention program between intervention and control group, the change of scores on knowledge were significantly different between the control group and the intervention group(p<0.05) and the change of scores on the attitude toward smoking was significantly different between intervention and control group. The change of smoking rate were not showing a significant difference between two groups but the amount of smoking were significantly reduced in intervention group than control group. In multiple regression analysis on changes of knowledge about smoking, the variables of smoking prevention program education, previous knowledge on smoking and students' school performance were selected the significant variables. In multiple regression to analysis of the factors influencing changes in attitude toward smoking, the variables of smoking prevention program education, previous knowledge on smoking were shown to be significant. The smoking prevention program was effective on change of knowledge and attitude of middle school students. In considering that the policy should be needed to extent of implementation of school-based health education curricula based on social influence model and it would contribute to reduce smoking of students.

• Tuberculosis and Respiratory Diseases
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• v.42 no.6
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• pp.923-933
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• 1995

Background: Pleural abnormality is the the most common respiratory change caused by asbestos dust inhalation and also develop other asbestos related disease after cessation of asbestos exposure. So we conducted epidemiologic study to investigate if the pleural abnormality is associated with pulmonary function change and what factors are influenced on pulmonary function impairment. Methods: Two hundred and twenty two asbestos workers from 9 industries using asbestos in Korea were selected to measure the concentration of sectional asbestos fiber. Ouestionnaire, chest X-ray, PFT were also performed. All the data were analyzed by student t-test and chi-square test using SAS. Regressional analysis was performed to evaluate important factors, for example smoking, exposure concentration, period and the existence of pleural thickening, affecting to the change of pulmonary function. Results: 1) All nine industries except two, airborn asbestos fiber concentration was less than an average permissible concentration. PFT was performed on 222 workers and the percentage of male was 88.3%, their mean age was $41{\pm}9$ years old, and the duration of asbestos exposure was $10.6{\pm}7.8$ yrs. 2) The chest X-ray showed normal(89.19%), pulmonary Tb(inactive)(2.7%), pleral thickening (7.66%), suspected reticulonodular shadow(0.9%). 3) The mean values of height, smoking status, concentration of asbestos fiberwere not different between the subjects with pleural thickening and others, but age, cumulative pack-years, the duration of asbestos exposure were higher in subjects with pleural thickening. 4) All the PFT indices were lower in the subjects with pleural thickening than in the subjects without pleural thickening. 5) Simple regression analysis showed there was a significant correlation between $FEF_{75}$ which is sensitive in small airway obstruction and cumulative smoking pack-years, the duration of asbestos exposure and the concentration of asbestos fiber. 6) Multiple regression analysis showed all the pulmonary function indices were decreased as the increase of cumulative smoking pack-years and especially in the indices those are sensitive in small airway obstruction. Pleural thickening was associated with reduction in FVC, $FEV_1$, PEFR and $FEF_{25}$. Conclusion: The more concentration of asbestos fiber and the more duration of asbestos exposure, the greater reduction in $FEF_{50}$, $FEF_{75}$. Therefore PFT was important in the evaluation of early detection for small airway obstruction. Furthermore pleural thickening without asbesto-related parenchymal lung disease is associated with reduction in pulmonary function.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.29-44
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• 2017

In Internet commerce, consumers are heavily influenced by product reviews written by other users who have already purchased the product. However, as the product reviews accumulate, it takes a lot of time and effort for consumers to individually check the massive number of product reviews. Moreover, product reviews that are written carelessly actually inconvenience consumers. Thus many online vendors provide mechanisms to identify reviews that customers perceive as most helpful (Cao et al. 2011; Mudambi and Schuff 2010). For example, some online retailers, such as Amazon.com and TripAdvisor, allow users to rate the helpfulness of each review, and use this feedback information to rank and re-order them. However, many reviews have only a few feedbacks or no feedback at all, thus making it hard to identify their helpfulness. Also, it takes time to accumulate feedbacks, thus the newly authored reviews do not have enough ones. For example, only 20% of the reviews in Amazon Review Dataset (Mcauley and Leskovec, 2013) have more than 5 reviews (Yan et al, 2014). The purpose of this study is to analyze the factors affecting the usefulness of online product reviews and to derive a forecasting model that selectively provides product reviews that can be helpful to consumers. In order to do this, we extracted the various linguistic, psychological, and perceptual elements included in product reviews by using text-mining techniques and identifying the determinants among these elements that affect the usability of product reviews. In particular, considering that the characteristics of the product reviews and determinants of usability for apparel products (which are experiential products) and electronic products (which are search goods) can differ, the characteristics of the product reviews were compared within each product group and the determinants were established for each. This study used 7,498 apparel product reviews and 106,962 electronic product reviews from Amazon.com. In order to understand a review text, we first extract linguistic and psychological characteristics from review texts such as a word count, the level of emotional tone and analytical thinking embedded in review text using widely adopted text analysis software LIWC (Linguistic Inquiry and Word Count). After then, we explore the descriptive statistics of review text for each category and statistically compare their differences using t-test. Lastly, we regression analysis using the data mining software RapidMiner to find out determinant factors. As a result of comparing and analyzing product review characteristics of electronic products and apparel products, it was found that reviewers used more words as well as longer sentences when writing product reviews for electronic products. As for the content characteristics of the product reviews, it was found that these reviews included many analytic words, carried more clout, and related to the cognitive processes (CogProc) more so than the apparel product reviews, in addition to including many words expressing negative emotions (NegEmo). On the other hand, the apparel product reviews included more personal, authentic, positive emotions (PosEmo) and perceptual processes (Percept) compared to the electronic product reviews. Next, we analyzed the determinants toward the usefulness of the product reviews between the two product groups. As a result, it was found that product reviews with high product ratings from reviewers in both product groups that were perceived as being useful contained a larger number of total words, many expressions involving perceptual processes, and fewer negative emotions. In addition, apparel product reviews with a large number of comparative expressions, a low expertise index, and concise content with fewer words in each sentence were perceived to be useful. In the case of electronic product reviews, those that were analytical with a high expertise index, along with containing many authentic expressions, cognitive processes, and positive emotions (PosEmo) were perceived to be useful. These findings are expected to help consumers effectively identify useful product reviews in the future.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.45-67
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• 2017

According to the Internet Usage Research performed in 2016, the number of internet users and the internet usage have been increasing. Smartphone, compared to the computer, is taking a more dominant role as an internet access device. As the number of smart devices have been increasing, some views that the demand on high-speed internet will decrease; however, Despite the increase in smart devices, the high-speed Internet market is expected to slightly increase for a while due to the speedup of Giga Internet and the growth of the IoT market. As the broadband Internet market saturates, telecom operators are over-competing to win new customers, but if they know the cause of customer exit, it is expected to reduce marketing costs by more effective marketing. In this study, we analyzed the relationship between the cancellation rates of telecommunication products and the factors affecting them by combining the data of 3 cities, Anyang, Gunpo, and Uiwang owned by a telecommunication company with the regional data from KOSIS(Korean Statistical Information Service). Especially, we focused on the assumption that the neighboring areas affect the distribution of the cancellation rates by coupling type, so we conducted spatial cluster analysis on the 3 types of cancellation rates of each region using the spatial analysis tool, SatScan, and analyzed the various relationships between the cancellation rates and the regional data. In the analysis phase, we first summarized the characteristics of the clusters derived by combining spatial information and the cancellation data. Next, based on the results of the cluster analysis, Variance analysis, Correlation analysis, and regression analysis were used to analyze the relationship between the cancellation rates data and regional data. Based on the results of analysis, we proposed appropriate marketing methods according to the region. Unlike previous studies on regional characteristics analysis, In this study has academic differentiation in that it performs clustering based on spatial information so that the regions with similar cancellation types on adjacent regions. In addition, there have been few studies considering the regional characteristics in the previous study on the determinants of subscription to high-speed Internet services, In this study, we tried to analyze the relationship between the clusters and the regional characteristics data, assuming that there are different factors depending on the region. In this study, we tried to get more efficient marketing method considering the characteristics of each region in the new subscription and customer management in high-speed internet. As a result of analysis of variance, it was confirmed that there were significant differences in regional characteristics among the clusters, Correlation analysis shows that there is a stronger correlation the clusters than all region. and Regression analysis was used to analyze the relationship between the cancellation rate and the regional characteristics. As a result, we found that there is a difference in the cancellation rate depending on the regional characteristics, and it is possible to target differentiated marketing each region. As the biggest limitation of this study and it was difficult to obtain enough data to carry out the analyze. In particular, it is difficult to find the variables that represent the regional characteristics in the Dong unit. In other words, most of the data was disclosed to the city rather than the Dong unit, so it was limited to analyze it in detail. The data such as income, card usage information and telecommunications company policies or characteristics that could affect its cause are not available at that time. The most urgent part for a more sophisticated analysis is to obtain the Dong unit data for the regional characteristics. Direction of the next studies be target marketing based on the results. It is also meaningful to analyze the effect of marketing by comparing and analyzing the difference of results before and after target marketing. It is also effective to use clusters based on new subscription data as well as cancellation data.

• Korean Journal of Soil Science and Fertilizer
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• v.27 no.1
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• pp.33-39
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• 1994

This survey was conducted to investigate the factors affecting on salt accumulation and chemical components of soils cultivated with horticulture crops in plastic film houses. The soil samples were taken from 40 sites in the northern central areas of Korea and were analyzed for the chemical properties and soil separates. The data were evaluated with soil texture and years of cultivation as major factors. The results are summarized as follows : 1. The chemical properties of surface soils in plastic film house were pH 5.80, EC $3.59mScm^{-1}$, O.M. 4.20%, Av. $P_2O_5$ 1,178ppm, $NO_3-N$ 180ppm, Av. $SO_4{^{2-}}$ 353ppm, $Cl^-$ 240ppm, Ex. Na 0.40me/100g. 2. Compared to the outside soil of plastic film house, the inside soil had 2.5~3 times higher contents of $NO_3-N$, Av. $SO_4{^{2-}}$ and $Cl^-$, 1.2~1.8 times higher exchangeable base elements, and 2.8 times higher electrical conductivity. But pH value of the inside soil was lower than the outside soil by 0.3 pH unit. 3. Soil texture classification showed that sandy loam, loam and silt loam were 32.5 %, 37.5 %, and 30.0 %, respectively. The contents of $NO_3-N$, Av. $SO_4{^{2-}}$, $NH_4-N$ and EC value were very high in silt loam soils. Av. $P_2O_5$ content and pH value of sandy loam soils were higher than those of silt loam and loam soils. 4. The contents of O.M. and Av. $P_2O_5$ were higher in long term cultivation, but the contents of $NO_3-N$, Av. $SO_4{^{2-}}$, $Cl^-$, Ex. Mg and Ex. Na including EC of the soil with 2~4 years cultivation were higher than those of the soil with above 5 years cultivation. 5. Multiple linear regression analysis showed that contribution degree of soil chemical properties to the EC was high in the order of $NO_3-N$ > Av. $SO_4{^{2-}}$ > Ex. Na > $Cl^-$ > Av. $P_2O_5$ > $NH_4-N$ > Ex. Mg>Ex. Ca. Among the soil chemical properties the contribution of anions was remarkably high. 6. EC value correlated with ${\sum}A$(total content of anions)as $r=0.932^{**}$ and with ${\sum}C$(total content of cations) as $r=0.452^{**}$.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.70-82
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• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.8
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• pp.542-548
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• 2014

Effects of bromide ($Br^-$) and iodide ($I^-$) concentrations, chlorine ($Cl_2$) doses, pH, temperature, ammonia nitrogen concentrations, reaction times and water characteristics on formation of iodinated trihalomethanes (I-THMs) during oxidation of iodide containing water with chlorine were investigated in this study. Results showed that the yields of I-THMs increased with the high bromide and iodide level during chlorination. The elevated pH significantly increased the yields of I-THMs during chlorination. The formation of I-THMs was higher at $20^{\circ}C$ than $4^{\circ}C$, $10^{\circ}C$ and $30^{\circ}C$. In chloramination study, addition of ammonium chloride ($NH_4Cl$) markedly increased the formation of I-THMs. Among the water samples collected from seven water sources including wastewater treatment plant (WWTP) effluent water (EfOM water), prepared humic containing water (HA water) and algal organic matter (AOM) containing water (AOM water), EfOM water generated the highest yields of I-THMs ($12.31{\mu}g/mg$ DOC), followed by HA water ($4.96{\mu}g/mg$ DOC), while AOM water produced the lowest yields of I-THMs ($0.99{\mu}g/mg$ DOC). $SUVA_{254}$ values of EfOM water, HA water and AOM water were $1.38L/mg{\cdot}m$, $4.96L/mg{\cdot}m$ and $0.97L/mg{\cdot}m$, respectively. The I-THMs yields had a low correlation with $SUVA_{254}$ values ($r^2$ = 0.002).

• Korean Journal of Soil Science and Fertilizer
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• v.12 no.4
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• pp.179-187
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• 1980

1) Soils (volcanic ash and muck) were fractionated into particle-size separates (200 - 20, 20 - 2, 2 - 0.5 and finer than $0.5{\mu}$ in diamter), and of which the silt fraction was further fractionated into specific gravity separates (more than 2.0, 2.0 - 1.7, 1.7 - 1.4 and less than 1.4 in $g/cc^3$). And total organic and inorganic phosphorus in the separates were determined. 2) The amounts of total, organic and inorganic phosphorus distributed in the particle-size separates were as follows fine clay > coarse clay > silt > fine sand fraction. The increase rate in the amounts of phosphorus was great in the separates finer than $20{\mu}$, and greatest in the fine clay fraction. 3) The amounts of total, oganic and inorganic phosphorus distributed in the specific gravity separates were as follows: 2.0 - 1.7 > 1.7 - 1.4 > heavier than 2.0 fraction. The increase rate in the amounts of phosphorus was in the following order 2.0 - 1.7 > 1.7 - 1.4 > heavier than 2.0 fraction. 4) Distribution of carbon, amorphous aluminum and free iron oxides in the particle-size separates and the specific gravity separates were examined, and the distribution and the formes of organo-minera1 complexes in the separates were discussed to shed light on the factors affecting the distribution of phosphorous into the separates. And it was estimated that there was close relation among the distribution of organic and inorganic phosphorus, and the distribution and the formes of organo-minera1 complexes.

• Journal of Chest Surgery
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• v.41 no.3
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• pp.347-353
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• 2008

Background: Esophageal perforation is an emergency that. requires early diagnosis and effective treatment. A delay in diagnosis and treatment. significantly increases morbidity and mortality. Material and Method: Thirty-seven patients with esophageal perforation were surgically treated at our institutions between January 1990 and December 2006. We retrospectively reviewed the results of surgical treatment for esophageal perforation to understand the risk factors affecting survival inpatients. Result: Patients ranged in age from 21 to 87 years, with an average age of $52.7{\pm}16.98$ years. Thirty-one of the patients were men and six were women. There were 23 patients (62%) with spontaneous perforations, 10 patients (27%) with a traumatic perforation, and 4 patients (11%) with an iatrogenic perforation. The site of esophageal perforation was the cervical esophagus in 5 patients, the thoracic esophagus in 31 patients, and the abdominal esophagus in one patient. Twenty-nine patients underwent primary closure of the perforation and five patients had T-tube drainage. Exclusion-diversion procedures were performed in two patients and an esophagectomy was performed in one patient. There were six cases of mortality (16.22%) and 25 cases of postoperative complications in 15 patients (40.5%). Patients that were treated later than 24 hours after detection of the perforation showed a statistically significant high morbidity and mortality rate (p<0.05). Conclusion: The most important risk factor of esophageal perforation was the time interval between detection of the perforation and the initiation of treatment. A prompt diagnosis and effective treatment are necessary to decrease morbidity and mortality.

• Journal of Chest Surgery
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• v.41 no.3
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• pp.354-359
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• 2008

Background: Warfarin is used as an anticoagulant and it is mainly excreted by the liver metabolism (the R-form is mainly metabolized by cytochrome p450 3A4, and the S form by cytochrome p450 2C9). Rifampin is usually used for tuberculosis or endocarditis, and it is a representative drug that induces the CYP families, including 3A4 and 2C9. The anticoagulation effect of warfarin decreases through the increased metabolism that's due to the induction of enzymes, and this iscaused by rifampin when patients take these two medicines together. No one has suggested appropriate guidelines regarding this drug interaction even though an appropriate adjustment of warfarin's dosage is needed. We examined the drug interaction in patients who received warfarin-rifampin combination therapy according to the time interval, and the factors affecting drug interaction were analyzed. Based on the data, we tried to determine the clinically available warfarin dosage guidelines before and after taking this drug combination. Material and Method: We reviewed the OO University Hospital anticoagulation service team's follow up sheets that were filled out from Jan '1998 to Sep 2006 for the patient who took warfarin - rifampin combination therapy (n=15). Result: The average INR of all the patient before rifampin administration was $2.25{\pm}0.52$ $(mean{\pm}SD)$, and that value for the first 100 days after rifampin administration was $1.98{\pm}0.28$. The p value for these two sets of data showed no correlation (paired t-test, p>0.05). The average INR of all the patient before rifampin cessation was $2.19{\pm}0.34$, and the value after rifampin cessation was $2.49{\pm}0.43$. The p value of these two showed correlation (paired t-test, p<0.05) but the average INR falls between the therapeutic INR range. Conclusion: The warfarin dose adjustment equation of before and after warfarin-rifampin combination therapy was derived based on this study's results because the warfarin dosage adjustment of the anticoagulation service team was considered appropriate.