• Tuberculosis and Respiratory Diseases
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• v.47 no.6
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• pp.824-835
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• 1999

Background: Sleep-related breathing disorders are commonly found in patients with chronic renal failure and particularly, sleep apnea may have an influence on the long-term mortality rates in these patients. Maintenance hemodialysis is the mainstay of medical measures for correcting the metabolic derangements of chronic renal failure but it is uncertain whether it may alleviate sleep disorders including sleep apnea. Methods: Forty seven patients on maintenance hemodialysis were surveyed with the sleep questionnaire about their clinical symptoms related to sleep disorders. Among them, 15 patients underwent the polysomnography and their blood levels of urea nitrogen, creatinine, electrolytes and the arterial blood gases in the nights before and following hemodialysis were measured. Results: Forty(85.1%) of the 47 patients complained of the symptoms associated with sleep-wake cycle disturbances, 55.3% experienced snoring and 27.7% reported witnessed apneas. The duration of REM sleep increased significantly in the nights after hemodialysis compared to the nights without hemodialysis(p<0.05) and the percentage of total sleep time comprising NREM sleep decreased significantly in the nights following hemodialysis compared to the nights before hemodialysis(p<0.05). The percentage of total sleep time consisting of the stage 1 and 2 NREM sleep showed the trend for a decrease in the nights after hemodialysis(p=0.051), while the percentage of total sleep time comprising the stage 3 and 4 NREM sleep did not change between nights. The obstructive sleep apnea was more predominant type than the central one in both nights and there were no differences in the apnea index and the apnea-hypopnea index between the nights. The decrease in the blood level of urea nitrogen, creatinine, potassium and phosphorus was observed after hemodialysis(p<0.05), but the differences of parameters measured during polysomnography between the nights did not correlate with the changes of biochemical factors obtained on the two nights. Arterial blood gas analysis showed that pH was significantly greater in the nights after hemodialysis than in the nights before hemodialysis(p<0.05), but there were no correlations between the parameters examined during polysomnography and the parameters of arterial blood gas analysis(p<0.05). Conclusion: These results suggest that chronic renal failure is an important systemic disorder which is strongly associated with sleep disorders. Maintenance hemodialysis, although it is a widely accepted measure to treat chronic renal failure, did not significantly modulate the sleep architecture and the severity of sleep apnea. Thus, taking the patients with chronic renal failure into account, it is advisable to try not only to find a substantial way for correcting metabolic derangements but also to consider the institution of more effective treatments for sleep disorders.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.1-21
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• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.