• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.153-169
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• 2012

Exhibitions have played a key role of effective marketing activity which directly informs services and products to current and potential customers. Through participating in exhibitions, exhibitors have got the opportunity to make face-to-face contact so that they can secure the market share and improve their corporate images. According to this economic importance of exhibitions, show organizers try to adopt a new IT technology for improving their performance, and researchers have also studied services which can improve the satisfaction of visitors through analyzing visit patterns of visitors. Especially, as smart technologies make them monitor activities of visitors in real-time, they have considered booth recommender systems which infer preference of visitors and recommender proper service to them like on-line environment. However, while there are many studies which can improve their performance in the side of new technological development, they have not considered the choice factor of visitors for booth recommender systems. That is, studies for factors which can influence the development direction and effective diffusion of these systems are insufficient. Most of prior studies for the acceptance of new technologies and the continuous intention of use have adopted Technology Acceptance Model (TAM) and Extended Technology Acceptance Model (ETAM). Booth recommender systems may not be new technology because they are similar with commercial recommender systems such as book recommender systems, in the smart exhibition environment, they can be considered new technology. However, for considering the smart exhibition environment beyond TAM, measurements for the intention of reuse should focus on how booth recommender systems can provide correct information to visitors. In this study, through literature reviews, we draw factors which can influence the satisfaction and reuse intention of visitors for booth recommender systems, and design a model to forecast adaptation of visitors for booth recommendation in the exhibition environment. For these purposes, we conduct a survey for visitors who attended DMC Culture Open in November 2011 and experienced booth recommender systems using own smart phone, and examine hypothesis by regression analysis. As a result, factors which can influence the satisfaction of visitors for booth recommender systems are the effectiveness, perceived ease of use, argument quality, serendipity, and so on. Moreover, the satisfaction for booth recommender systems has a positive relationship with the development of reuse intention. For these results, we have some insights for booth recommender systems in the smart exhibition environment. First, this study gives shape to important factors which are considered when they establish strategies which induce visitors to consistently use booth recommender systems. Recently, although show organizers try to improve their performances using new IT technologies, their visitors have not felt the satisfaction from these efforts. At this point, this study can help them to provide services which can improve the satisfaction of visitors and make them last relationship with visitors. On the other hands, this study suggests that they managers along the using time of booth recommender systems. For example, in the early stage of the adoption, they should focus on the argument quality, perceived ease of use, and serendipity, so that improve the acceptance of booth recommender systems. After these stages, they should bridge the differences between expectation and perception for booth recommender systems, and lead continuous uses of visitors. However, this study has some limitations. We only use four factors which can influence the satisfaction of visitors. Therefore, we should development our model to consider important additional factors. And the exhibition in our experiments has small number of booths so that visitors may not need to booth recommender systems. In the future study, we will conduct experiments in the exhibition environment which has a larger scale.

• Tuberculosis and Respiratory Diseases
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• v.49 no.6
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• pp.724-732
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• 2000

Background : In patients with severe chronic lung diseases even a small pneumothorax can result in life-threatening respiratory distress. It is important to treat the attack by chest tube drainage until the lung expands. Pneumothorax with a persistent air leak that does not resolve under prolonged tube thoracostomy suction is usually treated by open operation to excise or oversew a bulla or cluster of blebs to stop the air leak. Pleurodesis by the instillation of chemical agents is used for the patient who has persistent air leak and is not good candidate for surgical treatment. When the primary trial of pleurodesis with common agent fails, it is uncertain which agent should be used f or stopping the air leak by pleurodesis. It is well known that inappropriate drainage of hemothorax results in severe pleural adhesion and thickening. Based on this idea, some reports described a successful treatment with autologous blood instillation for pneumothorax patients with or without residual pleural space. We tried pleurodesis with autologous bood for pneumothorax with persistent air leak and then we evaluated the efficacy and safety. Methods : Fifteen patients who had persistent air leak in the pneumothorax complicated from the severe chronic lung disease were enrolled. They were not good candidates for surgical treatment and doxycycline pleurodesis failed to stop up their air leaks. We used a mixture of autologous blood and 50% dextrose for pleurodesis. Effect and complications were assessed by clinical out∞me, chest radiography and pulmonary function tests. Results : The mean duration of air leak was 18.4${\pm}$6.16 days before ABP (autologous blood and dextrose pleurodesis) and $5.2{\pm}1.68$ days after ABP. The mean severity of pain was $2.3{\pm}0.70$ for DP(doxycycline pleurodesis) and $1.7{\pm}0.59$ for ABDP (p<0.05). There was no other complication except mild fever. Pleural adhesion grade was a mean of $0.6{\pm}0.63$. The mean dyspnea scale was $1.7{\pm}0.46$ before pneumothrax and $2.0{\pm}0.59$ after ABDP (p>0.05). The mean $FEV_1$ was $1.47{\pm}1.01$ before pneumothorax and $1.44{\pm}1.00$ after ABDP (p>0.05). Except in 1 patient, 14 patients had no recurrent pneumothorax. Conclusion : Autologous blood pleurodesis (ABP) was successful for treatment of persistent air leak in the pneumothorax. It was easy and inexpensive and involved less pain than doxycycline pleurodesis. It did not cause complications and severe pleural adhesion. We report that ABP can be considered as a useful treatment for persistent air leak in the pneumothorax complicated from the severe chronic lung disease.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Korean Journal of Poultry Science
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• v.7 no.2
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• pp.54-76
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• 1980

A survey was conducted to determine the status of health and productivity of poultry farms in Korea. Area included Was Kyunggido where exist nearly 50% of national poultry population. From this area, 41 layer and 34 broiler farms covering 21 Countries were selected randomly for the survey. When farms were divided in the operation size, 95.1% of layer and 82.3% of broiler farms were classified as business or industrial level while the rest were managed in a small scale as part time job. Generally layer farms had been established much earlier than broiler farms. Geographically 10.7% of layer farms were sited near the housing area such as field foreast and rice field. No farms were located near the seashore. The distance from one farm from the other was very close, being 80% of the farms within the distance of 1km and as many as 28% of the farms within loom. This concentrated poultry farming in a certain area created serious problems for the sanitation and preventive measures, especially in case of outbreak of infectious diseases. Average farm size was 5,016${\times}$3.3㎡ for layers and 1,037${\times}$3.3㎡ for broilers. 89.5% of layer ana 70.6% of broiler farms owned the land for farming while the rest were on lease. In 60% of layer farms welters were employed for farming while in the rest their own labour was used. Majority of farms were equipped poorly for taking necessary practice of hygiene and sanitation. The amount of disinfectant used by farms was considerably low. As many as 97.6% of lave. farms were practised with Newcastle(ND) and fowl pox(F$.$pox) vaccine, whereas only 43.6% and 5.1% of broiler farms were practised with ND and F$.$pox vaccine, respectively. In 17-32.7% of farms ND vaccine was used less than twice until 60 days of age and in only 14.6% of farms adult birds were vaccinated every 4months. Monthly expense for preventive measures was over 200,000W in 32% of farms. Only 4.9-2.7% of vaccine users were soaking advice from veterinarians before practising vaccination, 85% of the users trusted the efficacy of the vaccines. Selection of medicine was generally determined by the farm owner rather than by veterinarans on whom 33.3% of farms were dependant. When diseases outbroke, 49.3% of farms called for veterinary hospital and the rest were handled by their own veterinarians, salesmen or professionals. Approximately 70% of farms were satisfied with the diagnosis made by the veterinarians. Frequency of disease outbreaks varied according to the age and type of birds. The livabilities of layers during the period of brooding, rearing ana adultwere 90.5, 98.9 and 75.2%, respectively while the livalibility of broilers until marketing was 92.2%. In layers, average culling age, was 533.3 day and hen housed eggs were 232.7. Average feed conversion rates of layers and broilers were 3.30 and 2.48, respectively. Those figures were considerably higher than anticipated but still far lower than those in developed countries.

• Korean Journal of Heritage: History & Science
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• v.53 no.4
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• pp.150-169
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• 2020

Surveys of Buddhist ruins at Namsan in Gyeongju began in earnest during the Japanese colonial era, undertaken by Japanese scholars. These surveys of Buddhist remains in Namsan made during the colonial period should be seen as previous research which cannot be ignored in any in-depth study of Buddhist ruins in Gyeongju. Full-scale surveys of Buddhist ruins at Namsan began in the 1920s. Previous surveys conducted around the time of the Japanese annexation of Korea in 1910 are generally viewed as only representing preliminary investigations and, thus, have not received much attention. However, these early surveys are significant in that they led to the Buddhist ruins on Namsan becoming widely known in the 1910s and served as the foundations for later studies. The removal of the Seated Medicine Buddha from Samneung Valley in Gyeongju in 1915 and its subsequent exhibition at the Joseon Local Products Expo, which marked the fifth anniversary of the Japanese administration of Korea, was especially important in garnering attention for Namsan's wealth of Buddhist artifacts, as the statue was placed in the main hall of the art museum and attracted a great deal of interest from visitors. It is typically thought that this Seated Medicine Buddha was exhibited in 1915 because it was the most beautiful and well-preserved statue from Namsan. However, the removal of this statue was closely related to the proposed move of the Seokguram statue to Seoul around the time of Korea's annexation. The plan to move Seokguram to Seoul was primarily devised by Terauchi Masatake, and the plan, based on Ilseontongjo-ron ('日鮮同祖論'), a historical theory that prehistoric Korean and Japanese people were of the same blood, and Joseon Jeongcheasoeng-ron ('朝鮮停滯性論'), a historical theory arguing that development had stagnated in Korea, was intended to be a visual demonstration of a new era for Korea. This new era was to proceed under the rule of the Japanese Empire through the dissolution of Gyeongbokgung, the symbol of the Joseon Dynasty, which would be replaced with past glories as symbolized by the statue of Buddha. However, as the plan floundered, the replacement for Seokguram in Seoul ended up being none other than the Seated Medicine Buddha of Samneung Valley. Surveys of the Seated Medicine Buddha began in 1911, administered by Sekino Tadashi, but he likely learned of the statue's location from Moroga Hideo or Kodaira Ryozo, Japanese residents of Gyeongju. It is also probable that these Japanese residents received a request from the Japanese Government General of Korea to find a Buddha statue that was worthy of being displayed at exhibitions. In this way, we can say that the transfer of the Seated Medicine Buddha to Seoul was the result of close cooperation between the Government General, Sekino Tadashi, and Japanese residents of Gyeongju. This also had the effect of removing the magical veil which had shrouded the Buddhist ruins of Namsan. In other words, while the early surveys of Buddhist ruins on Namsan are significant, it is difficult to argue that the surveys were undertaken for purely academic purposes, as they were deeply related to the imperial ambitions of Governor-General Terauchi which encompassed the plans to move Seokguram to Seoul and the successful hosting of the 1915 Expo. It should also be pointed out that the failure of the plan to move Seokguram to Seoul and the preservation of the Seated Stone Buddha of Mireuggok at Namsan was in no small part due to resistance from Korean residents in Gyeongju. Although it is not described in detail in the paper, research is needed which shows that the Korean residents of Gyeongju were not simple bystanders, but agents of history.