• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.3
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• pp.464-477
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• 2013

There is a great need to develop a training program proven to change behavior and improve knowledge. The purpose of this study was to evaluate employee hygiene knowledge, hygiene practice, and cleanliness, before and after HACCP system implementation at one small-size restaurant. The efficiency of the system was analyzed using time-temperature control after implementation of u-HACCP$^{(R)}$. The employee hygiene knowledge and practices showed a significant improvement (p<0.05) after HACCP system implementation. In non-heating processes, such as seasoned lettuce, controlling the sanitation of the cooking facility and the chlorination of raw ingredients were identified as the significant CCP. Sanitizing was an important CCP because total bacteria were reduced 2~4 log CFU/g after implementation of HACCP. In bean sprouts, microbial levels decreased from 4.20 logCFU/g to 3.26 logCFU/g. There were significant correlations between hygiene knowledge, practice, and microbiological contamination. First, personnel hygiene had a significant correlation with 'total food hygiene knowledge' scores (p<0.05). Second, total food hygiene practice scores had a significant correlation (p<0.05) with improved microbiological qualities of lettuce salad. Third, concerning the assessment of microbiological quality after 1 month, there were significant (p<0.05) improvements in times of heating, and the washing and division process. On the other hand, after 2 months, microbiological was maintained, although only two categories (division process and kitchen floor) were improved. This study also investigated time-temperature control by using ubiquitous sensor networks (USN) consisting of an ubi reader (CCP thermometer), an ubi manager (tablet PC), and application software (HACCP monitoring system). The result of the temperature control before and after USN showed better thermal management (accuracy, efficiency, consistency of time control). Based on the results, strict time-temperature control could be an effective method to prevent foodborne illness.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.75-100
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• 2018

Mobile devices have become an important channel for news content usage in our daily life. However, online news content readers' resistance to online news monetization is more serious than other digital content businesses, such as webtoons, music sources, videos, and games. Since major portal sites distribute online news content free of charge to increase their traffics, customers have been accustomed to free news content; hence this makes online news providers more difficult to switch their policies on business models (i.e., monetization policy). As a result, most online news providers are highly dependent on the advertising business model, which can lead to increasing number of false, exaggerated, or sensational advertisements inside the news website to maximize their advertising revenue. To reduce this advertising dependencies, many online news providers had attempted to switch their 'free' readers to 'paid' users, but most of them failed. However, recently, some online news media have been successfully applying the Pay-What-You-Want (PWYW) payment model, which allows readers to voluntarily pay fees for their favorite news content. These successful cases shed some lights to the managers of online news content provider regarding that the PWYW model can serve as an alternative business model. In this study, therefore, we collected 379 online news articles from Ohmynews.com that has been successfully employing the PWYW model, and analyzed the comparative importance of systematic attributes of online news content on readers' voluntary payment. More specifically, we derived the six systematic attributes (i.e., Type of Article Title, Image Stimulation, Article Readability, Article Type, Dominant Emotion, and Article-Image Similarity) and three or four levels within each attribute based on previous studies. Then, we conducted content analysis to measure five attributes except Article Readability attribute, measured by Flesch readability score. Before conducting main content analysis, the face reliabilities of chosen attributes were measured by three doctoral level researchers with 37 sample articles, and inter-coder reliabilities of the three coders were verified. Then, the main content analysis was conducted for two months from March 2017 with 379 online news articles. All 379 articles were reviewed by the same three coders, and 65 articles that showed inconsistency among coders were excluded before employing conjoint analysis. Finally, we examined the comparative importance of those six systematic attributes (Study 1), and levels within each of the six attributes (Study 2) through conjoint analysis with 314 online news articles. From the results of conjoint analysis, we found that Article Readability, Article-Image Similarity, and Type of Article Title are the most significant factors affecting online news readers' voluntary payment. First, it can be interpreted that if the level of readability of an online news article is in line with the readers' level of readership, the readers will voluntarily pay more. Second, the similarity between the content of the article and the image within it enables the readers to increase the information acceptance and to transmit the message of the article more effectively. Third, readers expect that the article title would reveal the content of the article, and the expectation influences the understanding and satisfaction of the article. Therefore, it is necessary to write an article with an appropriate readability level, and use images and title well matched with the content to make readers voluntarily pay more. We also examined the comparative importance of levels within each attribute in more details. Based on findings of two studies, two major and nine minor propositions are suggested for future empirical research. This study has academic implications in that it is one of the first studies applying both content analysis and conjoint analysis together to examine readers' voluntary payment behavior, rather than their intention to pay. In addition, online news content creators, providers, and managers could find some practical insights from this research in terms of how they should produce news content to make readers voluntarily pay more for their online news content.

• Tuberculosis and Respiratory Diseases
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• v.42 no.2
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• pp.206-217
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• 1995

Background: In patients with obstructive sleep apnea syndrome(OSAS), there are several factors increasing upper airway resistance and there is a predisposition to compromised respiratory function during waking and sleep related to constitutional factors including a tendency to obesity. Several recent studies have suggested a possible relationship between sleep apnea(SA) and systemic hypertension. But the possible pathophysiologic link between SA and hypertension is still unclear. In this study, we have examined the relationship among age, body mass index(BMI), pulmonary function parameters and polysomnographic data in patients with OSAS. And also we tried to know the difference among these parameters between hypertensive OSAS and normotensive OSAS patients. Methods: Patients underwent a full night of polysomnography and measured pulmonary function during waking. OSAS was diagnosed if patients had more than 5 apneas per hour(apnea index, AI). A careful history of previously known or present hypertension was obtained from each patient, and patients with systolic blood pressure $\geq$ 160mmHg and/or diastolic blood pressure $\geq$ 95mmHg were classified as hypertensives. Results: The noctural nadir of arterial oxygen saturation($SaO_2$ nadir) was negatively related to AI and respiratory disturbance index(RDI), and the degree of noctural oxygen desaturation(DOD) was positively related to AI and RDI. BMI contributed to AI, RDI, $SaO_2$ nadir and DOD values. And also BMI contributed to $FEV_1,\;FEV_1/FVC$ and DLco values. There was a correlation between airway resistance(Raw) and AI, and there was a inverse correlation between DLco and DOD. But there was no difference among these parameters between hypertensive OSAS and normotensive OSAS patients. Conclusion: The obesity contributed to the compromised respiratory function and the severity of OSAS. AI and RDI were important factors in the severity of hypoxia during sleep. The measurement of pulmonary function parameters including Raw and DLco may be helpful in the prediction and assessment of OSAS patients. But we could not find clear difference between hypertensive and normotensive OSAS patients.

• Korean Journal of Agricultural and Forest Meteorology
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• v.8 no.1
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• pp.1-9
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• 2006

Regardless of the recent observed warmer winters in Korea, more freeze injuries and associated economic losses are reported in fruit industry than ever before. Existing freeze-frost forecasting systems employ only daily minimum temperature for judging the potential damage on dormant flowering buds but cannot accommodate potential biological responses such as short-term acclimation of plants to severe weather episodes as well as annual variation in climate. We introduce 'dormancy depth', in addition to daily minimum temperature, as a complementary criterion for judging the potential damage of freezing temperatures on dormant flowering buds of grape vines. Dormancy depth can be estimated by a phonology model driven by daily maximum and minimum temperature and is expected to make a reasonable proxy for physiological tolerance of buds to low temperature. Dormancy depth at a selected site was estimated for a climatological normal year by this model, and we found a close similarity in time course change pattern between the estimated dormancy depth and the known cold tolerance of fruit trees. Inter-annual and spatial variation in dormancy depth were identified by this method, showing the feasibility of using dormancy depth as a proxy indicator for tolerance to low temperature during the winter season. The model was applied to 10 vineyards which were recently damaged by a cold spell, and a temperature-dormancy depth-freeze injury relationship was formulated into an exponential-saturation model which can be used for judging freeze risk under a given set of temperature and dormancy depth. Based on this model and the expected lowest temperature with a 10-year recurrence interval, a freeze risk probability map was produced for Hwaseong County, Korea. The results seemed to explain why the vineyards in the warmer part of Hwaseong County have been hit by more freeBe damage than those in the cooler part of the county. A dormancy depth-minimum temperature dual engine freeze warning system was designed for vineyards in major production counties in Korea by combining the site-specific dormancy depth and minimum temperature forecasts with the freeze risk model. In this system, daily accumulation of thermal time since last fall leads to the dormancy state (depth) for today. The regional minimum temperature forecast for tomorrow by the Korea Meteorological Administration is converted to the site specific forecast at a 30m resolution. These data are input to the freeze risk model and the percent damage probability is calculated for each grid cell and mapped for the entire county. Similar approaches may be used to develop freeze warning systems for other deciduous fruit trees.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• Journal of the Korean Society of Food Science and Nutrition
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• v.42 no.1
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• pp.102-113
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• 2013

The purpose of this study was to examine parental perceptions on the importance, performance level, and satisfaction with foodservice quality at daycare centers in the Compensation and Welfare Service institute. The questionnaire was developed to measure thirty-two attributes of foodservice operations are administered to 598 parents and 23 foodservice supervisors from June 22, 2009 to July 10, 2009. The parents placed a high importance on the need for foodservices, earning 4.70 points out of 5 points. Their perceptions of foodservice quality menu, foodservice ingredients and effects, facilities, sanitation, and service scored even higher than performance. The overall satisfaction level for foodservice compared to performance was 4.33 and 4.03 points out of 5 points, respectively. Multiple regression analysis indicated that 98.6% of the variance in parents' overall satisfaction scores was explained by six dimensions.

• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 1998.10a
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• pp.83-106
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• 1998

This study was conducted to investigate the probiotics(acid and bile resistance, fermentation properties, viability, cholesterol assimilation, antimicrobial activity, antimutagenicity, and immunoactivation) of the strains of bifidobacteria isolated from healthy Koreans and to investigate the effects of oral administration of Bifidobacterium longum A-2 on the fecal microflora, ${\beta}-glucuronidase$ activity, pH values, Ammonia concentration. The experimental results are summarized as follows: The probiotics were tested for 23 strains including three commer챠al strains as controls. Compared to other strains, strains of A-2 and A-9 showed more acid resistance whereas A-2, A-5, A-13, A-14, A-18 and A-22 showed excellent bile resistances. The properties of bifidobacteria during fermentation were tested. Strains of A-1, A-2, A-3, A-4, A-6, and A-23 resulted in less than pH 4.5 and titratable acidity over 0.90 after 24 hr of fermentation. When the strains of A-2 was grown with glucose, maltose, and fructooligosaccharide, the acetic acid production were higher than with sorbitol and mannitol. The storage stability of the strains of A-2 and A-22 were tesed, indicating the strain A-2 was more stable over 10 days of storage at both $4^{\circ}C$ and $20^{\circ}C$ than A-22. The strains of A-8, A-10, A-11, A-12 and A-20 assimilated more than 30% of cholesterol included in the media. The strains of A-1 and A-2 showed antimicrobial activity against Sta. aureus. The antimutagenicity of the strains were also tested, showing that the mutation was suppressed more by three strains(A-2, A-12, and A-23). In addition, strain A-5 improved immunological activity(phagocytosis, $TNF-{\alpha}$, IL-6) more than other strains. In the effects of oral administration of Bif. longum A-2, the number of fecal bifidobacteria was siginificantly increased(p<0.01) and the level of fecal ${\beta}-glucuronidase$ also was siginificantly reduced(p<0.05). However there were no siginificant differences in the level of Lαctobacilli, Enterobacteriaceae, Clostridium perfringens, pH and ammonia by the administration. The results suggested that Bif. longum A-2 may be met the criteria for probiotics culture.

• Journal of Animal Science and Technology
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• v.54 no.1
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• pp.1-8
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• 2012

This study aimed to investigate the single nucleotide polymorphisms (SNPs) of the porcine MC4R gene and validate the effect of the MC4R genotype for marker assisted selection (MAS). Six amplicons were produced to analyze the entire base sequences of the porcine MC4R gene and six SNPs were detected (c.-780C>G, c.-135C>T, c.175C>T-Leu59Leu, c.707A>G-Arg236His, c.892A>G-Asp298Asn, and c.*430A>T). Linkage disequilibrium (LD) of the six SNPs was analyzed by performing haploid analysis. There was a perfect linkage disequilibrium in c.-780C>G, c.-135C>T, c.175C>T-Leu59Leu, c.707A>G-Arg236His, and c.*430A>T. Only the c.892A>G (Asp298Asn) SNP showed a very low LD with an $r^2$ value of 0.028 and the D' value of 0.348. As a result, the two SNPs-c.707A>G (Arg236His) and c.892A>G (Asp298Asn)-were selected to extract the genotype frequencies from the 5 pig breeds by using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) genotype analysis method. The SNP frequency of c.707A>G (Arg236His) indicated the presence of the A (His) allele only in Yorkshire, while the G allele was fixed in the KNP, Landrace, Berkshire, and Duroc. Association analysis was carried out in 484 pigs with the c.707A>G (Arg236His) SNP and the meat quality traits of four different pig cross populations: a significant association was noted in crude fat, sirloin moisture, meat color, and the degree of red and yellow coloration. The frequency of the c.892A>G(Asp298Asn) SNP genotype varied among the breeds; while Duroc showed the highest frequency of the A (Asn) allele, KNP showed the highest frequency of the G (Asp) allele. Association analysis was carried out in 1126 pigs with the c.892A>G (Asp298Asn) SNP and the meat quality traits of four pig populations: a highly significant linkage was noted in the back-fat thickness (P<0.002). It was found that the back-fat thickness was higher in individuals with the AA genotype than in those with the AG or GG genotype. Thus, in this study, we verified that the c.892A>G (Asp298Asn) SNP in the pig MC4R gene has a sufficient effect as a gene marker for MAS in Korean pork industry.

• Journal of Yeungnam Medical Science
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• v.21 no.2
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• pp.151-166
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• 2004

Background: Spirituality has been an important part of Transpersonal Psychology and is believed to have a large effect on the mental health because it has been systematized. The aim of this study was to determine the level of spiritual disposition on human beings along with its effects on one's mental health. Materials and Methods: The study targeted 400 women residing in Youn-Gil city of JiLin Prov., which is a district of the Cho-Sun tribe in China. Their spiritual well-being was studied using the Spiritual Well-being Scale-Korean Version. The spiritual well-being scale consists of 2 sub-scales of religious well-being and existential well-being. The study was evaluated using a lie scale, psychotic trend, and a combined anxiety-depression scale. The results were considered to be factors of one's mental health. The correlation between the spiritual well-being and each tendency was analyzed by regression analysis. Results: The total score of the Cho-Sun tribal women according to the spiritual well-being scale was 68.29 which was much less than the 100.65 of Korean Christian women. There was no significant correlation between the spiritual well-being and the Lie trend. However, it was found that 86%(344) of Cho-Sun tribal women scored above 70 in the Lie trend with a mean score of 74.57 which is higher than normal populations. Regarding the correlation between the spiritual well-being and psychotic trend, the psychotic trend became significantly higher when the religious well-being was at a high level. On the other hand, the psychotic trend became significantly lower when the existential well-being was at a high level. Regarding the correlation between the spiritual well-being and anxiety, the anxiety was significantly higher when the religious well-being was at a high level. However, the anxiety level was significantly low when the existential well-being was at a high level. Regarding the correlation between the spiritual well-being and depression, the depression level was somewhat significantly high when the religious well-being was at a high level. However, the depression level was significantly low when the existential well-being was at a high level. Conclusion: This study evaluated the effects of spiritual well-being on a person's mental health among Cho-Sun tribal women in Youn-Gil city of JiLIn Prov., P.R. of China. The results found that the religious well-being, which is a sub-scale of spiritual well-being, had negative effects while the existential well-being had positive effects on the mental health. These results proved that a person's religious disposition had negative effects on their mental health in a communitarian society.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.