• Tuberculosis and Respiratory Diseases
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• v.43 no.6
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• pp.987-996
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• 1996

Background : We prospectively evaluated the applicability and effect of noninvasive ventilation (NIV) in acute respiratory failure and tried to find out the parameters that could predict successful application of NIV. Methods : Twenty-six out of 106 patients with either acute ventilatory failure (VF: $PaCO_2$ > 43 mm Hg with pH < 7.35) or oxygenation failure (OF: $PaO_2/AO_2$ < 300 mm Hg with $pH{\geq}7.35$) requiring mechanical ventilation were managed by NIV (CPAP + pressure suppon, or BiPAP) with face mask. Eleven out of 19 cases with VF (57.9%) (M : F=7 : $55.4{\pm}14.6$ yrs) and 15 out of 87 cases with OF (17.2%) (M : F=12 : 3, $50.6{\pm}15.6$ yrs) were s uilable for NIY. Respiratory rates, arterial blood gases and success rate of NIV were analyzed in each group. Results: 81.8% (9/11) of YF and 40% (6/15) of OF were successfully managed on NIV and were weruled from mechanical ventilator without resorting to endotracheal intubation. Complications were noted in 2 cases (nasal skin necrosis 1, gaseous gastric distension 1). In NIV for ventilatory failure, the respiration rate was significantly decreased at 12 hour of NIV ($34{\pm}9$ /min pre-NIV, $26{\pm}6$ /min at 12 hour of NIV, p=0.045), while $PaCO_2$ ($87.3{\pm}20.6$ mm Hg pre-NIV, $81.2{\pm}9.1$ mm Hg at 24 hour of NIV) and pH ($7.26{\pm}0.04$, $7.32{\pm}0.02$, respectively, p <0.05) were both significantly decreased at 24 hour of NIV In NIV for oxygenation failure, $PaCO_2$ were not different between the successful and the failed cases at pre-NIV and till 12 hours after NIV. The $PaO_2/FIO_2$ ratio, however, significantly improved at 0.5 hour of NIV in successful cases and were maintained at around 200 mm Hg (n=6 : at baseline, 0.5h, 6h, 12h : $120.0{\pm}19.6$, $218.9{\pm}98.3$, $191.3{\pm}55.2$, $232.8{\pm}17.6$ mm Hg, respectively, p=0.0211), but it did not rise in the failed cases (n=9 : $127.9{\pm}63.0$, $116.8{\pm}24.4$, $100.6{\pm}34.6$, $129.8{\pm}50.3$ mm Hg, respectively, p=0.5319). Conclusion : From the above results we conclude that NIV is effective for hypercapnic respiratory failure and its success was heralded by reduction of respiration rale before the reduction in $PaCO_2$ level. In hypoxic respiratory failure, NIV is much less effective, and the immediate improvement of $PaO_2/FIO_2$ ratio at 0.5h after application is thought to be a predictor of successful NIV.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• 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 Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.3
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• pp.1-40
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• 1965

To measure variations in some of the important agronomic characteristics of rice varieties under shifting of seedling dates, this study has been carried out at the Paddy Crop Division of Crop Experiment Station(then Agricultural Experiment Station) in Suwon for the period of three years 1958 to 1960. The varieties used in this study were Kwansan, Suwon ＃82, Mojo, Paltal and Chokwang, which have the different agronomic characteristics such as earliness and plant type. Seeds of each variety were sown at 14 different dates in 10-day interval starting on March 2. The seedlings were grown on seed bed for 30, 40, 50, 60, 70 and 80 days, respectively. The results of this study are as follows: A. Heading dates. 1. As the seeding date was delayed, the heading dates was almost proportionally delayed. The degree of delay was higher in early varieties and lower in late varieties and the longer the seedling stage, the more delayed the heading date. 2. Number of days to heading was proportionally lessened as seeding was delayed in all the varieties but the magnitude varied depending upon variety. In other words, the required period for heading in case of late planting was much shortened in late variety compared with early one. Within a variety, the number of days to heading was less shortened as the seedling stage was prolonged. Early variety reached earlier than late variety to the marginal date for the maximum shortening of days to heading and the longer the seeding stage, the limitted date came earlier. There was a certain limit in seeding date for shortening of days to heading as seeding was delayed, and days to heading were rather prolonged due to cold weather when seeded later than that date. 3. In linear regression equation, Y=a+bx obtained from the seeding dates and the number of days to heading, the coefficient b(shortening rate of days to heading) was closely correlated with the average number of days to heading. That is, the period from seeding to heading was more shortened in late variety than early one as seeding was delayed. 4. To the extent that the seedling stage is not so long and there is a linear relationship between delay of seeding and shortening of days to heading, it might be possible to predict heading date of a rice variety to be sown any date by using the linear regression obtained from variation of heading dates under the various seeding dates of the same variety. 5. It was found out that there was a close correlation between the numbers of days to heading in ordinary culture and the other ones. When a rice variety was planted during the period from the late part of March to the middle of June and the seedling ages were within 30 to 50 days, it could be possible to estimate heading date of the variety under late or early culture with the related data of ordinary culture. B. Maturing date. 6. Within (he marginal date for maturation of rice variety, maturing date was proportionally delayed as heading was delayed. Of course, the degree of delay depended upon varieties and seedling ages. The average air temperature (Y) during the ripening period of rice variety was getting lower as the heading date. (X) was delayed. Though there was a difference among varieties, in general, a linear regression equation(y=25.53-0.182X) could be obtained as far as heading date were within August 1 to September 13. 7. Depending upon earliness of a rice variety, the average air temperature during the ripening period were greatly different. Early variety underwent under 28$^{\circ}C$ in maximum while late variety matured under as low as 22$^{\circ}C$. 8. There was a highly significant correlation between the average air temperature (X) during the ripening period, and number of day (Y) for the maturation. And the relationship could be expressed as y=82.30-1.55X. When the average air temperature during the period was within the range of 18$^{\circ}C$ to 28$^{\circ}C$, the ripening period was shortened by 1.55 days with increase of 1$^{\circ}C$. Considering varieties, Kwansan was the highest in shortening the maturing period by 2.24 days and Suwon ＃82 was the lowest showing 0.78 days. It is certain that ripening of rice variety is accelerated at Suwon as the average air temperature increases within the range of 18$^{\circ}C$ to 28$^{\circ}C$. 9. Between number of days to heading (X) related to seeding dates and the accumulated average air temperature (Y) during the ripening period, a positive correlation was obtained. However, there was a little difference in the accumulated average air temperature during the ripening period even seeding dates were shifted to a certain extent. C. Culm- and ear-lengths. 10. In general all the varieties didn't show much variation in their culm-lengths in case of relatively early seeding but they trended to decrease the lengths as seeding was delayed. The magnitude of decreasing varied from young seedlings to old ones. Young seedlings which were seeded during May 21 to June 10 didn't decrease their culm-lengths, while seedlings old as 80 days decreased the length though under ordinary culture. 11. Variation in ear-length of rice varieties show the same trend as the culm-length subjected to the different seeding dates. When rice seedlings aged from 30 to 40 days, the ear-length remained constant but rice plants older than 40 days obviously decreased their ear-lengths. D. Number of panicles per hill. 12. The number of panicles per hill decreased up to a certain dates as seeding was delayed and then again increased the panicles due to the development of numerous tillers at the upper internodes. The seeding date to reach to the least number of panicles of rice variety depended upon the seedling ages. Thirty- to 40-day seedlings which were seeded during May 31 to June 10 developed the lowest number of panicles and 70- to 80-day seedlings sown for the period from April 11 to April 21 reached already to the minimum number of panicles. E. Number of rachillae. 13. To a certain seeding date, the number of rachillae didn't show any variation due to delay of seeding but it decreased remarkably when seeded later than the marginal date. 14. Variation in number of rachillae depended upon seedling ages. For example, 30- to 40-day old seedlings which, were originally seeded after May 31 started to decrease the rachillae. On the other hand, 80-day old seedlings which, were seeded on May 1 showed a tendency to decrease rachillae and the rice plant sown on May 31 could develop narrowly 3 or 4 panicles. F. Defective grain and 1.000-grain weights. 15. Under delay of the seeding dates, weight of the defective grains gradually increased till a certain date and then suddenly increased. These relationships could be expressed with two different linear regressions. 16. If it was assumed that the marginal date for ripening was the cross point of these two lines, the date seemed. closely related with seedling ages. The date was June 10- in 30- to 40-day old seedlings but that of 70- to 80-day old seedlings was May 1. Accordingly, the marginal date for ripening was getting earlier as the seedling stage was prolonged. 17. The 1.000-grain weight in ordinary culture was the heaviest and it decreased in both early and late cultures. G. Straw and rough rice weights. 18. Regardless of earliness of variety, rice plants under early culture which were seeded before March 22 or April 1 did not show much variation in straw weight due to seedling ages but in ordinary culture it gradually decreased and the degree was became greater in late culture. 19. Relationship between seeding dates (X) and grain weight related to varieties and seedling ages, could be expressed as a parabola analogous to a line (Y=77.28-7.44X$_1$-1.00lX$_2$). That is, grain yield didn't vary in early culture but it started to decrease when seeded later than a certain date, as seeding was delayed. The variation was much greater in cases of late planting and prolongation of seedling age. 20. Generally speaking, the relationship between grain yield (Y) and number of days to heading (X) was described with linear regression. However, the early varieties were the highest yielders within the range of 60 to 110, days to heading but the late variety greatly decreased its yield since it grows normally only under late culture. The grain yield, on the whole, didn't increase as number of days to heading exceeded more than 140 days.