• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.69-76
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• 2013

The incidence of globally infectious and pathogenic diseases such as H1N1 (swine flu) and Avian Influenza (AI) has recently increased. An infectious disease is a pathogen-caused disease, which can be passed from the infected person to the susceptible host. Pathogens of infectious diseases, which are bacillus, spirochaeta, rickettsia, virus, fungus, and parasite, etc., cause various symptoms such as respiratory disease, gastrointestinal disease, liver disease, and acute febrile illness. They can be spread through various means such as food, water, insect, breathing and contact with other persons. Recently, most countries around the world use a mathematical model to predict and prepare for the spread of infectious diseases. In a modern society, however, infectious diseases are spread in a fast and complicated manner because of rapid development of transportation (both ground and underground). Therefore, we do not have enough time to predict the fast spreading and complicated infectious diseases. Therefore, new system, which can prevent the spread of infectious diseases by predicting its pathway, needs to be developed. In this study, to solve this kind of problem, an integrated monitoring system, which can track and predict the pathway of infectious diseases for its realtime monitoring and control, is developed. This system is implemented based on the conventional mathematical model called by 'Susceptible-Infectious-Recovered (SIR) Model.' The proposed model has characteristics that both inter- and intra-city modes of transportation to express interpersonal contact (i.e., migration flow) are considered. They include the means of transportation such as bus, train, car and airplane. Also, modified real data according to the geographical characteristics of Korea are employed to reflect realistic circumstances of possible disease spreading in Korea. We can predict where and when vaccination needs to be performed by parameters control in this model. The simulation includes several assumptions and scenarios. Using the data of Statistics Korea, five major cities, which are assumed to have the most population migration have been chosen; Seoul, Incheon (Incheon International Airport), Gangneung, Pyeongchang and Wonju. It was assumed that the cities were connected in one network, and infectious disease was spread through denoted transportation methods only. In terms of traffic volume, daily traffic volume was obtained from Korean Statistical Information Service (KOSIS). In addition, the population of each city was acquired from Statistics Korea. Moreover, data on H1N1 (swine flu) were provided by Korea Centers for Disease Control and Prevention, and air transport statistics were obtained from Aeronautical Information Portal System. As mentioned above, daily traffic volume, population statistics, H1N1 (swine flu) and air transport statistics data have been adjusted in consideration of the current conditions in Korea and several realistic assumptions and scenarios. Three scenarios (occurrence of H1N1 in Incheon International Airport, not-vaccinated in all cities and vaccinated in Seoul and Pyeongchang respectively) were simulated, and the number of days taken for the number of the infected to reach its peak and proportion of Infectious (I) were compared. According to the simulation, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days when vaccination was not considered. In terms of the proportion of I, Seoul was the highest while Pyeongchang was the lowest. When they were vaccinated in Seoul, the number of days taken for the number of the infected to reach at its peak was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. When they were vaccinated in Pyeongchang, the number of days was the fastest in Seoul with 37 days and the slowest in Pyeongchang with 43 days. In terms of the proportion of I, Gangneung was the highest while Pyeongchang was the lowest. Based on the results above, it has been confirmed that H1N1, upon the first occurrence, is proportionally spread by the traffic volume in each city. Because the infection pathway is different by the traffic volume in each city, therefore, it is possible to come up with a preventive measurement against infectious disease by tracking and predicting its pathway through the analysis of traffic volume.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.7 no.1
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• pp.77-91
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• 1996

In order to elucidate the biological etiology and the effects of comorbidity on biological variables in tic disorders, plasma serotonin (5-hydroxlfryptamine, 5-HT) and 5-hydroxy- indoleacetic acid (5-HIAA) we.e measured in 87 tic disorders and 30 control subjects. The 87 tic disorder were composed of 45 Tourette's disorder(TS), 22 chronic motor tic disorders (CMT) and 20 transient tic disorders (TTD). Among these patients,43 patients were pure tic disorder (PT), 28 subject also had attention deficit hyperactivity disorder (T+ADHD) and 16 subjects had obsessive compulsive disorders (T+ OCD) as comorbid disorders. The results are summarized as follows : 1) Plasma 5-HT levels showed significant positive correlations with plasma 5-HIAA levels (Pennon r=0.77, p<0.05). 2) Plasma 5-HT and 5-HIAA levels showed no significant correlation with age in tic disorders. 3) Plasma 5-HIAA and 5-HT levels showed no significant correlations with age in control subjects. 4) There was significant difference in plasma 5-HT levels among TS, CMT, TTD and control groups (ANOVA F=34.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and ITD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TTD groups. 5) There was significant difference in plasma 5-HIAA levels among TS, CMT, TTD and control groups (ANOVA F=26.48, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed significant differences between control and TS, control and CMT, control and TTD groups. But, post-hoc test showed no significant differences between TS and CMT, TS and TTD, CMT and TID groups.f) There was significant difference in plasma 5-HT and 5-HIAA levels among PT, T+ADHD, T+OCD and contol groups (ANOVA 5-HT, F=37.59, df=3, 113, p<0.01, 5-HIAA, F=27.37, df=3, 113, p<0.01), and post-hoc test using Scheffe method showed signiscant differences between control and PT, control and T+ADHD and control and T+OCB. But, post-hoc test showed no significant differences between PT and T+ADHD, PT and T+ OCD and T+ADHD and T+ OCD. These results show that decreased 5-HT and 5-HIAA levels may play a role in the genesis of tic disorders, but these findings have no significant correlations with the severity of tic disorders. And the comorbid disorders of tics may have minimal effects on the biochemical abnormalities. Future studies must be focused on the effects of serotonin agonists and antagonists on tic disorders and molecular biological methodology may enhance to elucidate the mechanisms of these abnormal findings.

• Korean small business review
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• v.31 no.2
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• pp.103-120
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• 2009

As business incubation centers (BICs) have been operating for more than 10 years in Korea, many early stage startups tend to use the services provided by the incubating centers. BICs in Korea have accumulated the knowledge and experience in the past ten years and their services have been considerably improved. The business incubating service has three facets : (1) business infrastructure service, (2) direct service, and (3) indirect service. The mission of BICs is to provide the early stage entrepreneurs with the incubating service in a limited period time to help them grow strong enough to survive the fierce competition after graduating from the incubation. However, the incubating services sometimes fail to foster the independence of new startup companies, and raise the dependence of many companies on BICs. Thus, the dependence on BICs is a very important factor to understand the survival of the incubated startup companies after graduation from BICs. The purpose of this study is to identify the main factors that influence the firm's dependence on BICs and to characterize the relationships among the identified factors. The business incubating service is a core construct of this study. It includes various activities and resources, such as offering the physical facilities, legal service, and connecting them with outside organizations. These services are extensive and take various forms. They are provided by BICs directly or indirectly. Past studies have identified various incubating services and classify them in different ways. Based on the past studies, we classify the business incubating service into three categories as mentioned above : (1) business infrastructure support, (2) direct support, and (3) networking support. The business infrastructure support is to provide the essential resources to start the business, such as physical facilities. The direct support is to offer the business resources available in the BICs, such as human, technical, and administrational resources. Finally, the indirect service was to support the resource in the outside of business incubation center. Dependence is generally defined as the degree to which a client firm needs the resources provided by the service provider in order to achieve its goals. Dependence is generated when a firm recognizes the benefits of interacting with its counterpart. Hence, the more positive outcomes a firm derives from its relationship with the partner, the more dependent on the partner the firm must inevitably become. In business incubating, as a resident firm is incubated in longer period, we can predict that her dependence on BICs would be stronger. In order to foster the independence of the incubated firms, BICs have to be able to manipulate the provision of their services to control the firms' dependence on BICs. Based on the above discussion, the research model for relationships between dependence and its affecting factors was developed. We surveyed the companies residing in BICs to test our research model. The instrument of our study was modified, in part, on the basis of previous relevant studies. For the purposes of testing reliability and validity, preliminary testing was conducted with firms that were residing in BICs and incubated by the BICs in the region of Gwangju and Jeonnam. The questionnaire was modified in accordance with the pre-test feedback. We mailed to all of the firms that had been incubated by the BICs with the help of business incubating managers of each BIC. The survey was conducted over a three week period. Gifts (of approximately ₩10,000 value) were offered to all actively participating respondents. The incubating period was reported by the business incubating managers, and it was transformed using natural logarithms. A total of 180 firms participated in the survey. However, we excluded 4 cases due to a lack of consistency using reversed items in the answers of the companies, and 176 cases were used for the analysis. We acknowledge that 176 samples may not be sufficient to conduct regression analyses with 5 research variables in our study. Each variable was measured through multiple items. We conducted an exploratory factor analysis to assess their unidimensionality. In an effort to test the construct validity of the instruments, a principal component factor analysis was conducted with Varimax rotation. The items correspond well to each singular factor, demonstrating a high degree of convergent validity. As the factor loadings for a variable (or factor) are higher than the factor loadings for the other variables, the instrument's discriminant validity is shown to be clear. Each factor was extracted as expected, which explained 70.97, 66.321, and 52.97 percent, respectively, of the total variance each with eigen values greater than 1.000. The internal consistency reliability of the variables was evaluated by computing Cronbach's alphas. The Cronbach's alpha values of the variables, which ranged from 0.717 to 0.950, were all securely over 0.700, which is satisfactory. The reliability and validity of the research variables are all, therefore, considered acceptable. The effects of dependence were assessed using a regression analysis. The Pearson correlations were calculated for the variables, measured by interval or ratio scales. Potential multicollinearity among the antecedents was evaluated prior to the multiple regression analysis, as some of the variables were significantly correlated with others (e.g., direct service and indirect service). Although several variables show the evidence of significant correlations, their tolerance values range between 0.334 and 0.613, thereby demonstrating that multicollinearity is not a likely threat to the parameter estimates. Checking some basic assumptions for the regression analyses, we decided to conduct multiple regression analyses and moderated regression analyses to test the given hypotheses. The results of the regression analyses indicate that the regression model is significant at p < 0.001 (F = 44.260), and that the predictors of the research model explain 42.6 percent of the total variance. Hypotheses 1, 2, and 3 address the relationships between the dependence of the incubated firms and the business incubating services. Business infrastructure service, direct service, and indirect service are all significantly related with dependence (β = 0.300, p < 0.001; β = 0.230, p < 0.001; β = 0.226, p < 0.001), thus supporting Hypotheses 1, 2, and 3. When the incubating period is the moderator and dependence is the dependent variable, the addition of the interaction terms with the antecedents to the regression equation yielded a significant increase in R2 (F change = 2.789, p < 0.05). In particular, direct service and indirect service exert different effects on dependence. Hence, the results support Hypotheses 5 and 6. This study provides several strategies and specific calls to action for BICs, based on our empirical findings. Business infrastructure service has more effect on the firm's dependence than the other two services. The introduction of an additional high charge rate for a graduated but allowed to stay in the BIC is a basic and legitimate condition for the BIC to control the firm's dependence. We detected the differential effects of direct and indirect services on the firm's dependence. The firms with long incubating period are more sensitive to indirect service positively, and more sensitive to direct service negatively, when assessing their levels of dependence. This implies that BICs must develop a strategy on the basis of a firm's incubating period. Last but not least, it would be valuable to discover other important variables that influence the firm's dependence in the future studies. Moreover, future studies to explain the independence of startup companies in BICs would also be valuable.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.95-118
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• 2017

Recent centrally the downtown area, the transaction between the row housing and multiplex housing is activated and platform services such as Zigbang and Dabang are growing. The row housing and multiplex housing is a blind spot for real estate information. Because there is a social problem, due to the change in market size and information asymmetry due to changes in demand. Also, the 5 or 25 districts used by the Seoul Metropolitan Government or the Korean Appraisal Board(hereafter, KAB) were established within the administrative boundaries and used in existing real estate studies. This is not a district classification for real estate researches because it is zoned urban planning. Based on the existing study, this study found that the city needs to reset the Seoul Metropolitan Government's spatial structure in estimating future housing prices. So, This study attempted to classify the area without spatial heterogeneity by the reflected the property price characteristics of row housing and Multiplex housing. In other words, There has been a problem that an inefficient side has arisen due to the simple division by the existing administrative district. Therefore, this study aims to cluster Seoul as a new area for more efficient real estate analysis. This study was applied to the hedonic model based on the real transactions price data of row housing and multiplex housing. And the K-Means Clustering algorithm was used to cluster the spatial structure of Seoul. In this study, data onto real transactions price of the Seoul Row housing and Multiplex Housing from January 2014 to December 2016, and the official land value of 2016 was used and it provided by Ministry of Land, Infrastructure and Transport(hereafter, MOLIT). Data preprocessing was followed by the following processing procedures: Removal of underground transaction, Price standardization per area, Removal of Real transaction case(above 5 and below -5). In this study, we analyzed data from 132,707 cases to 126,759 data through data preprocessing. The data analysis tool used the R program. After data preprocessing, data model was constructed. Priority, the K-means Clustering was performed. In addition, a regression analysis was conducted using Hedonic model and it was conducted a cosine similarity analysis. Based on the constructed data model, we clustered on the basis of the longitude and latitude of Seoul and conducted comparative analysis of existing area. The results of this study indicated that the goodness of fit of the model was above 75 % and the variables used for the Hedonic model were significant. In other words, 5 or 25 districts that is the area of the existing administrative area are divided into 16 districts. So, this study derived a clustering method of row housing and multiplex housing in Seoul using K-Means Clustering algorithm and hedonic model by the reflected the property price characteristics. Moreover, they presented academic and practical implications and presented the limitations of this study and the direction of future research. Academic implication has clustered by reflecting the property price characteristics in order to improve the problems of the areas used in the Seoul Metropolitan Government, KAB, and Existing Real Estate Research. Another academic implications are that apartments were the main study of existing real estate research, and has proposed a method of classifying area in Seoul using public information(i.e., real-data of MOLIT) of government 3.0. Practical implication is that it can be used as a basic data for real estate related research on row housing and multiplex housing. Another practical implications are that is expected the activation of row housing and multiplex housing research and, that is expected to increase the accuracy of the model of the actual transaction. The future research direction of this study involves conducting various analyses to overcome the limitations of the threshold and indicates the need for deeper research.

• Applied Biological Chemistry
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• v.10
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• pp.69-100
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• 1968

1. In order to investigate on the microflora and enzyme activity of mold wheat 'Nuruk' , the major source of microorganisms for the brewing of Takju (a Korean Sake), two samples of Nuruk, one prepared at the College of Agriculture, Chung Nam University (S) and the other perchased at a market (T), were taken for the study. The molds, aerobic bacteria, lactic acid bacteria, and yeasts were examined and counted. The yeasts were classified by the treatment with TTC (2, 3, 5 triphenyltetrazolium chloride) agar that yields a varied shade of color. The amylase and protease activities of Nuruk were measured. The results were as the followings. a) In the Nuruk S found were: Aspergillus oryzae group, $204{\times}10^5$; Black Aspergilli, $163{\times}10^5$; Rhizogus, $20{\times}10^5$; Penicillia, $134{\times}10^5$; Areobic bacteria, $9{\times}10^6-2{\times}10^7$; Lactic acid bacteria, $3{\times}10^4$ In the Nuruk T found were: Aspergillus oryzae group, $836{\times}10^5$; Black Aspergilli, $286{\times}10^5$; Rhizopus, $623{\times}10^5$; Penicillia, $264{\times}10^5$; Aerobic bacteria, $5{\times}10^6-9{\times}10^6$; Lactic acid bacteria, $3{\times}10^4$ b) Eighty to ninety percent of the aerobic bacteria in Nuruk S appeared to belong to Bacillus subtilis while about 70% of those in Nuruk T seemed to be spherical bacteria. In both Nuruks about 80% of lactic acid bacteria were observed as spherical ones. c) The population of yeasts in 1g. of Nuruk S was about $6{\times}10^5$, 56.5% of which were TTC pink yeasts, 16% of which were TTC red pink yeasts, 8% of which were TTC red yeasts, 19.5% of which were TTC white yeasts. In Nuruk T(1g) the number of yeasts accounted for $14{\times}10^4$ and constituted of 42% TTC pink. 21% TTC red pink 28% TTC red and 9% TTC white. d) The enzyme activity of 1g Nuruk S was: Liquefying type Amylase, $D^{40}/_{30},=256$ W.V. Saccharifying type Amylase, 43.32 A.U. Acid protease, 181 C.F.U. Alkaline protease, 240C.F.U. The enzyme activity of 1g Nuruk T was: Liquefying type Amylase $D^{40}/_{30},=32$ W.V. Saccharifying type amylase $^{30}34.92$ A.U. Acid protease, 138 C.F.U. Alkaline protease 31 C.F.U. 2. During the fermentation of 'Takju' employing the Nuruks S and T the microflora and enzyme activity throughout the brewing were observed in 12 hour intervals. TTC pink and red yeasts considered to be the major yeasts were isolated and cultured. The strains ($1{\times}10^6/ml$) were added to the mashes S and T in which pH was adjusted to 4.2 and the change of microflora was examined during the fermentation. The results were: a) The molds disappeared from each sample plot since 2 to 3 days after mashing while the population of aerobic bacteria was found to be $10{\times}10^7-35{\times}10^7/ml$ inS plots and $8.2{\times}10^7-12{\times}10^7$ in plots. Among them the coccus propagated substantially until some 30 hours elasped in the S and T plots treated with lactic acid but decreased abruptly thereafter. In the plots of SP. SR. TP. and TR the coccus had not appeared from the beginning while the bacillus showed up and down changes in number and diminished by 1/5-1/10 the original at the end stage. b) The lactic acid bacteria observed in the S plot were about $7.4{\times}10^7$ in number per ml of the mash in 24 hours and increased up to around $2{\times}10^8$ until 3-4 days since. After this period the population decreased rapidly and reached about $4{\times}10^5$ at the end, In the plot T the lactic acid becteria found were about $3{\times}10^8$ at the period of 24 fours, about $3{\times}10$ in 3 days and about $2{\times}10^5$ at the end in number. In the plots SP. SR. TP, and TR the lactic acid bacteria observed were as less as $4{\times}10^5$ at the stage of 24 hours and after this period the organisms either remained unchanged in population or ceased to exist. c) The maiority of lactic acid bacteria found in each mash were spherical and the change in number displayed a tendency in accordance with the amount of lactic acid and alcohol produced in the mash. d) The yeasts had showed a marked propagation since the period of 24 hours when the number was about $2{\times}10^8$ ㎖ mash in the plot S. $4{\times}10^8$ in 48 hours and $5-7{\times}10^8$ in the end period were observed. In the plot T the number was $4{\times}10^8$ in 24 hours and thereafter changed up and down maintaining $2-5{\times}10^8$ in the range. e) Over 90% of the yeasts found in the mashes of S and T plots were TTC pink type while both TTC red pink and TTC red types held range of $2{\times}10-3{\times}10^7$ throughout the entire fermentation. f) The population of TTC pink yeasts in the plot SP was as $5{\times}10^8$ much as that is, twice of that of S plot at the period of 24 hours. The predominance in number continued until the middle and later stages but the order of number became about the same at the end. g) Total number of the yeasts observed in the plot SR showed little difference from that of the plot SP. The TTC red yeasts added appeared considerably in the early stage but days after the change in number was about the same as that of the plot S. In the plot TR the population of TTC red yeasts was predominant over the T plot in the early stage which there was no difference between two plots there after. For this reason even in the plot w hers TTC red yeasts were added TTC pink yeasts were predominant. TTC red yeasts observed in the present experiment showed continuing growth until the later stage but the rate was low. h) In the plot TP TTC pink yeasts were found to be about $5{\times}10^8$ in number at the period of 2 days and inclined to decrease thereafter. Compared with the plot T the number of TTC pink yeasts in the plot TP was predominant until the middle stage but became at the later stage. i) The productivity of alcohol in the mash was measured. The plot where TTC pink yeasts were added showed somewhat better yield in the earely stage but at and after the middle stage the difference between the yeast-added and the intact mashes was not recognizable. And the production of alcohol was not proportional to the total number of yeasts present. j) Activity of the liquefying amylase was the highest until 12 hours after mashing, somewhat lowered once after that, and again increased around 36-48 hours after mashing. Then the activity had decreased continuously. Activity of saccharifying amylase also decreased at the period of 24 hours and then increased until 48 hours when it reached the maximum. Since, the activity had gradually decreased until 72 hours and rapidly so did thereafter. k) Activity of alkaline protease during the fermentation of mash showed a tendency to decrease continusously although somewhat irregular. Activity of acid protease increased until hours at the maximum, then decreased rapidly, and again increased, the vigor of acid protease showed better shape than that of alkaline protease throughout. 3. TTC pink yeasts that were predominant in number, two strains of TTC red pink yeasts that appeared throughout the brewing, and TTC red yeasts were identified and the physiological characters examined. The results were as described below. a) TTC pinkyeasts (B-50P) and two strains of TTC red pink yeasts (B-54 RP & B-60 RP) w ere identified as the type of Saccharomyces cerevisiae and TTC pink red yeasts CB-53 R) were as the type of Hansenula subpelliculosa. b) The fermentability of four strains above mentioned were measured as follows. Two strains of TTC red pink yeasts were the highest, TTC pink yeasts were the lowest in the fermantability. The former three strains were active in the early stage of fermentation and found to be suitable for manufacturing 'Takju' TTC red yeasts were found to play an important role in Takju brewing due to its strong ability to produce esters although its fermentability was low. c) The tolerance against nitrous acid of strains of yeast was marked. That against lactic acid was only 3% in Koji extract, and TTC red yeasts showed somewhat stronger resistance. The tolerance against alcohol of TTC pink and red pink yeasts in the Hayduck solution was 7% while that in the malt extract was 13%. However, that of TTC red yeasts was much weaker than others. Liguefying activity of gelatin by those four strains of yeast was not recognized even in 40 days. 4. Fermentability during Takju brewing was shown in the first two days as much as 70-80% of total fermentation and around 90% of fermentation proceeded in 3-4 days. The main fermentation appeared to be completed during :his period. Productivity of alcohol during Takju brewing was found to be apporximately 65% of the total amount of starch put in mashing. 5. The reason that Saccharomyces coreanuss found be Saito in the mash of Takju was not detected in the present experiment is considered due to the facts that Aspergillus oryzae has been inoculated in the mold wheat (Nuruk) since around 1930 and also that Koji has been used in Takju brewing, consequently causing they complete change in microflora in the Takju brewing. This consideration will be supported by the fact that the original flavor and taste have now been remarkably changed.