• Radiation Oncology Journal
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• v.27 no.4
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• pp.181-188
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• 2009

Purpose: The aim of this study was to evaluate the results of postoperative radiotherapy in a case of perihilar cholagiocarcinoma by analyzing overall survival rate, patterns of failure, prognostic factors for overall survival, and toxicity. Materials and Methods: Between January 1998 and March 2008, 38 patients with perihilar cholangiocarcinoma underwent a surgical resection and adjuvant radiotherapy. The median patient age was 59 years (range, 28 to 72 years), which included 23 men and 15 women. The extent of surgery was complete resection in 9 patients, microscopically positive margins in 25 patients, and a subtotal resection in 4 patients. The tumor bed and regional lymphatics initially received 45 Gy or 50 Gy, but was subsequently boosted to a total dose of 59.4 Gy or 60 Gy in incompletely resected patients. The median radiotherapy dose was 59.4 Gy. Concurrent chemotherapy was administered in 30 patients. The median follow-up period was 14 months (range, 6 to 45 months). Results: The 3-year overall survival and 3-year progression free survival rates were 30% and 8%, respectively. The median survival time was 28 months. A multivariate analysis showed that differentiation was the only significant factor for overall survival. The 3-year overall survival was 34% in R0 patients and 20% in R1 patients. No statistically significant differences in survival were found between the 2 groups (p=0.3067). The first site of failure was local in 18 patients (47%). No patient experienced grade 3 or higher acute toxicity and duodenal bleeding developed in 2 patients. Conclusion: Our results suggest that adjuvant RT might be a significant factor in patients with a positive margin following a radical resection. However, there was still a high locoregional recurrence rate following surgery and postoperative radiotherapy. Further study is necessary to enhance the effect of the adjuvant radiotherapy.

• Tuberculosis and Respiratory Diseases
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• v.43 no.6
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• pp.945-953
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• 1996

Background : Many acute and chronic lung diseases including pneumoconiosis are characterized by the presence of increased numbers of activated macrophages. These macrophages generate several inflammatory cell chemoattractants, by which neutrophil migrate from vascular compartment to the alveolar space. Recruited neutrophils secrete toxic oxygen radicals or proteolytic enzymes and induce inflammatory response. Continuing inflammatory response results in alteration of the pulmonary structure and irreversible fibrosis. Recently, a polypeptide with specific neutrophil chemotactic activity, interleukin-8(IL-8), has been cloned and isolated from a number of cells including : monocytes, macrophages and fibroblasts. IL-1 and/or TNF-${\alpha}$ preceded for the synthesis of IL-8, and we already observed high level of IL-1 and TNF-${\alpha}$ in the pneumoconioses. So we hypothesized that IL-8 may be a central role in the pathogenesis of pneumoconiosis. In order to evaluate the clinical utility of IL-8 as a biomarker in the early diagnosis of pneumoconiosis, we investigated the increase of IL-8 in the pneumoconiotic patient and the correlation between IL-8 level and progression of pneumoconiosis. Method : We measured IL-8 in the serum of 48 patients with pneumoconiosis and 16 persons without dust exposure history as a control group. Pneumoconiotic cases were divided into 3 groups according to ILO Classification : suspicious group(n=16), small opacity group(n=16) and large opacity group(n=16). IL-8 was measured by a sandwich enzytne immunoassay technique. All data were expressed as the $mean{\pm}standard$ deviation. Results: 1) The mean value of age was higher in the small opacity and large opacity group than comparison group, but smoking history was even. Duration of dust exposure was not different among 3 pneumoconiosis groups. 2) IL-8 level was $70.50{\pm}53.63pg/m{\ell}$ in the suspicious group, $107.50{\pm}45.88pg/m{\ell}$ in the small opacity group, $132.50{\pm}73.47pg/m{\ell}$ in the large opacity group and $17.85{\pm}33.85pg/m{\ell}$ in the comparison group. IL-8 concentration in all pneumoconiosis group was significant higher than that in the comparison group(p<0.001). 3) IL-8 level tended to increase with the progression of pneumoconiosis. Multiple comparison test using Anova/Scheffe analysis showed a significant difference between suspicious group and large opacity group(p<0.05). 4) The level of IL-8 was correlated with the progression of pneumoconiosis(r=0.4199, p<0.05). Conclusion : IL-8 is thought to be a good biomarker for the early diagnosis of pneumoconiosis.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.79-96
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• 2012

As the pace of competition dramatically accelerates and the complexity of change grows, a variety of research have been conducted to improve firms' short-term performance and to enhance firms' long-term survival. In particular, researchers and practitioners have paid their attention to identify promising technologies that lead competitive advantage to a firm. Discovery of promising technology depends on how a firm evaluates the value of technologies, thus many evaluating methods have been proposed. Experts' opinion based approaches have been widely accepted to predict the value of technologies. Whereas this approach provides in-depth analysis and ensures validity of analysis results, it is usually cost-and time-ineffective and is limited to qualitative evaluation. Considerable studies attempt to forecast the value of technology by using patent information to overcome the limitation of experts' opinion based approach. Patent based technology evaluation has served as a valuable assessment approach of the technological forecasting because it contains a full and practical description of technology with uniform structure. Furthermore, it provides information that is not divulged in any other sources. Although patent information based approach has contributed to our understanding of prediction of promising technologies, it has some limitations because prediction has been made based on the past patent information, and the interpretations of patent analyses are not consistent. In order to fill this gap, this study proposes a technology forecasting methodology by integrating patent information approach and artificial intelligence method. The methodology consists of three modules : evaluation of technologies promising, implementation of technologies value prediction model, and recommendation of promising technologies. In the first module, technologies promising is evaluated from three different and complementary dimensions; impact, fusion, and diffusion perspectives. The impact of technologies refers to their influence on future technologies development and improvement, and is also clearly associated with their monetary value. The fusion of technologies denotes the extent to which a technology fuses different technologies, and represents the breadth of search underlying the technology. The fusion of technologies can be calculated based on technology or patent, thus this study measures two types of fusion index; fusion index per technology and fusion index per patent. Finally, the diffusion of technologies denotes their degree of applicability across scientific and technological fields. In the same vein, diffusion index per technology and diffusion index per patent are considered respectively. In the second module, technologies value prediction model is implemented using artificial intelligence method. This studies use the values of five indexes (i.e., impact index, fusion index per technology, fusion index per patent, diffusion index per technology and diffusion index per patent) at different time (e.g., t-n, t-n-1, t-n-2, ${\cdots}$) as input variables. The out variables are values of five indexes at time t, which is used for learning. The learning method adopted in this study is backpropagation algorithm. In the third module, this study recommends final promising technologies based on analytic hierarchy process. AHP provides relative importance of each index, leading to final promising index for technology. Applicability of the proposed methodology is tested by using U.S. patents in international patent class G06F (i.e., electronic digital data processing) from 2000 to 2008. The results show that mean absolute error value for prediction produced by the proposed methodology is lower than the value produced by multiple regression analysis in cases of fusion indexes. However, mean absolute error value of the proposed methodology is slightly higher than the value of multiple regression analysis. These unexpected results may be explained, in part, by small number of patents. Since this study only uses patent data in class G06F, number of sample patent data is relatively small, leading to incomplete learning to satisfy complex artificial intelligence structure. In addition, fusion index per technology and impact index are found to be important criteria to predict promising technology. This study attempts to extend the existing knowledge by proposing a new methodology for prediction technology value by integrating patent information analysis and artificial intelligence network. It helps managers who want to technology develop planning and policy maker who want to implement technology policy by providing quantitative prediction methodology. In addition, this study could help other researchers by proving a deeper understanding of the complex technological forecasting field.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.