The object of this study was 200 patients who had been treated in the Oriental Medical Hospital at Dong Eui Medical Center during 9 months from Jan. 1999 to sept. 1999. We proceeded the judgment of Sasang Constitution according to 'Questionnaire of Sasang Constitution Classification (I)', and 'Questionnaire of Sasang Constitution Classification II(QSCCII)' and the diagnosis by a medical specialist. The following conclusion were made in comparison with Sasang Constitution and Questionnaire. 1. We selected the 84 subjects what had the statistical value out of the 196 subjects('Questionnaire of Sasang Constitution Classification (I)' had the 71 subjects and 'Questionnaire of Sasang Constitution Classification II(QSCCII)', had the 121 subjects). And we selected again the 73 subjects('Questionnaire of Sasang Constitution Classification (I)', had the 33 subjects and 'Questionnaire of Sasang Constitution Classification II (QSCC II)' had the 40 subjects) out of the 84 subjects, because it had a repeated subjects. 2. We made the Questionnaire what has the 85 subjects, including the subjects what was approved its statistical value by 'A CLINICAL STUDY OF THE JUDGMENT OF SASANG CONSTITUTION ACCORDING TO QUESTIONNAIRE' and 'A CLINICAL STUDY OF THE TYPE OF DISEASE AND SYMPTOM ACCORDING TO SASANG CONSTITUTION CLASSIFICATION'. The subject what ask the physique and the body form was 7, the subject what ask the external appearance and the posture was 7, the subject what ask the habit and the character was 3, the subject what ask the physiology and the pathology was 3, the subject what ask the phenomenon that he has frequency was 4, the subject what ask the eating was 3, the subject what ask the symptom that he has frequency was 14, the subject what ask the work and the qualities-defects was 6, the subject what ask the friendly intercourse was 7, the subject what ask the usual mind was 5, the subject what ask the emotional inclination was I, the subject what ask the behavioral inclination was 10, the subject what ask the character was 15. 3. In the new Questionnaire, the subject what has relevance to Soyang was 84, the subject what has relevance to Soeum was 87, the subject what has relevance to Taeeum was 70. And we made the point of subject with the statistical ratio. The total point of Soyang was 7785.04, the total point of Soeum was 7742.80, the total point of Taeeum was 7746.60. 4. As a result of judgment of Sasang Constitution between the clinical diagnosis by a medical specialist and the new Questionnaire, the diagnostic accuracy of new Questionnaire was 73.33%. The diagnostic accuracy of Soyang was low, the others was high. And the Taeyang was excepted.
Purpose If a new test is introduced or reagents are changed in the laboratory of a medical institution, the characteristics of the test should be analyzed according to the procedure and the assessment of reagents should be made. However, several necessary conditions must be met to perform all required comparative evaluations, first enough samples should be prepared for each test, and secondly, various reagents applicable to the comparative evaluations must be supplied. Even if enough comparative evaluations have been done, there is a limit to the fact that the data variation for the new reagent represents the overall patient data variation, The fact puts a burden on the laboratory to the change the reagent. Due to these various difficulties, reagent changes in the laboratory are limited. In order to introduce a competitive bid, the institute conducted a full investigation of Radioimmunoassay(RIA) reagents for each test and established the range of reagents available in the laboratory through comparative evaluations. We wanted to share this process. Materials and Methods There are 20 items of tests conducted in our laboratory except for consignment tests. For each test, RIA reagents that can be used were fully investigated with the reference to external quality control report. and the manuals for each reagent were obtained. Each reagent was checked for the manual to check the test method, Incubation time, sample volume needed for the test. After that, the primary selection was made according to whether it was available in this laboratory. The primary selected reagents were supplied with 2kits based on 100tests, and the data correlation test, sensitivity measurement, recovery rate measurement, and dilution test were conducted. The secondary selection was performed according to the results of the comparative evaluation. The reagents that passed the primary and secondary selections were submitted to the competitive bidding list. In the case of reagent is designated as a singular, we submitted a explanatory statement with the data obtained during the primary and secondary selection processes. Results Excluded from the primary selection was the case where TAT was expected to be delayed at the moment, and it was impossible to apply to our equipment due to the large volume of reagents used during the test. In the primary selection, there were five items which only one reagent was available.(squamous cell carcinoma Ag(SCC Ag), β-human chorionic gonadotropin(β-HCG), vitamin B12, folate, free testosterone), two reagents were available(CA19-9, CA125, CA72-4, ferritin, thyroglobulin antibody(TG Ab), microsomal antibody(Mic Ab), thyroid stimulating hormone-receptor-antibody(TSH-R-Ab), calcitonin), three reagents were available (triiodothyronine(T3), Tree T3, Free T4, TSH, intact parathyroid hormone(intact PTH)) and four reagents were available are carcinoembryonic antigen(CEA), TG. In the secondary selection, there were eight items which only one reagent was available.(ferritin, TG, CA19-9, SCC, β-HCG, vitaminB12, folate, free testosterone), two reagents were available(TG Ab, Mic Ab, TSH-R-Ab, CA125, CA72-4, intact PTH, calcitonin), three reagents were available(T3, Tree T3, Free T4, TSH, CEA). Reasons excluded from the secondary selection were the lack of reagent supply for comparative evaluations, the problems with data reproducibility, and the inability to accept data variations. The most problematic part of comparative evaluations was sample collection. It didn't matter if the number of samples requested was large and the capacity needed for the test was small. It was difficult to collect various concentration samples in the case of a small number of tests(100 cases per month or less), and it was difficult to conduct a recovery rate test in the case of a relatively large volume of samples required for a single test(more than 100 uL). In addition, the lack of dilution solution or standard zero material for sensitivity measurement or dilution tests was one of the problems. Conclusion Comparative evaluation for changing test reagents require appropriate preparation time to collect diverse and sufficient samples. In addition, setting the total sample volume and reagent volume range required for comparative evaluations, depending on the sample volume and reagent volume required for one test, will reduce the burden of sample collection and planning for each comparative evaluation.
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.
Since 2000, the employment rate of small and medium enterprises (SMEs) has dwindled while the creation of new jobs and the emergence of healthy SMEs have been stagnant. The fundamental reason for these symptoms is that the economic structure is disadvantageous to SMEs. In particular, the greater gap between SMEs and large enterprises has resulted in polarization, and the resulting imbalance has become the largest obstacle to improving SMEs' competitiveness. For example, the total productivity has continued to drop, and the average productivity of SMEs is now merely 30% of that of large enterprises, and the average wage of SMEs' employees is only 53% of that of large enterprises. Along with polarization, rapid industrialization has also caused anti-enterprise consensus, the collapse of the middle class, hostility towards establishments, and other aftereffects. The general consensus is that unless these problems are solved, South Korea will not become an advanced country. Especially, South Korea is now facing issues that need urgent measures, such as the decline of its economic growth, the worsening distribution of profits, and the increased external volatility. Recognizing such negative trends, the MB administration proposed a win-win growth policy and recently introduced a new national value called "ecosystemic development." As the terms in such policy agenda are similar, however, the conceptual differences among such terms must first be fully understood. Therefore, in this study, the concepts of win-win growth policy and ecosystemic development, and the need for them, were surveyed, and their differences from and similarities with other policy concepts like win-win cooperation and symbiotic development were examined. Based on the results of the survey and examination, the study introduced a South Korean model of win-win growth, targeting the promotion of a sound balance between large enterprises and SMEs and an innovative ecosystem, and finally, proposing future policy tasks. Win-win growth is not an academic term but a policy term. Thus, it is less advisable to give a theoretical definition of it than to understand its concept based on its objective and method as a policy. The core of the MB administration's win-win growth policy is the creation of a partnership between key economic subjects such as large enterprises and SMEs based on each subject's differentiated capacity, and such economic subjects' joint promotion of growth opportunities. Its objective is to contribute to the establishment of an advanced capitalistic system by securing the sustainability of the South Korean economy. Such win-win growth policy includes three core concepts. The first concept, ecosystem, is that win-win growth should be understood from the viewpoint of an industrial ecosystem and should be pursued by overcoming the issues of specific enterprises. An enterprise is not an independent entity but a social entity, meaning it exists in relationship with the society (Drucker, 2011). The second concept, balance, points to the fact that an effort should be made to establish a systemic and social infrastructure for a healthy balance in the industry. The social system and infrastructure should be established in such a way as to create a balance between short- term needs and long-term sustainability, between freedom and responsibility, and between profitability and social obligations. Finally, the third concept is the behavioral change of economic entities. The win-win growth policy is not merely about simple transactional relationships or determining reasonable prices but more about the need for a behavior change on the part of economic entities, without which the objectives of the policy cannot be achieved. Various advanced countries have developed different win-win growth models based on their respective cultures and economic-development stages. Japan, whose culture is characterized by a relatively high level of group-centered trust, has developed a productivity improvement model based on such culture, whereas the U.S., which has a highly developed system of market capitalism, has developed a system that instigates or promotes market-oriented technological innovation. Unlike Japan or the U.S., Europe, a late starter, has not fully developed a trust-based culture or market capitalism and thus often uses a policy-led model based on which the government leads the improvement of productivity and promotes technological innovation. By modeling successful cases from these advanced countries, South Korea can establish its unique win-win growth system. For this, it needs to determine the method and tasks that suit its circumstances by examining the prerequisites for its success as well as the strengths and weaknesses of each advanced country. This paper proposes a South Korean model of win-win growth, whose objective is to upgrade the country's low-trust-level-based industrial structure, in which large enterprises and SMEs depend only on independent survival strategies, to a high-trust-level-based social ecosystem, in which large enterprises and SMEs develop a cooperative relationship as partners. Based on this objective, the model proposes the establishment of a sound balance of systems and infrastructure between large enterprises and SMEs, and to form a crenovative social ecosystem. The South Korean model of win-win growth consists of three axes: utilization of the South Koreans' potential, which creates community-oriented energy; fusion-style improvement of various control and self-regulated systems for establishing a high-trust-level-oriented social infrastructure; and behavioral change on the part of enterprises in terms of putting an end to their unfair business activities and promoting future-oriented cooperative relationships. This system will establish a dynamic industrial ecosystem that will generate creative energy and will thus contribute to the realization of a sustainable economy in the 21st century. The South Korean model of win-win growth should pursue community-based self-regulation, which promotes the power of efficiency and competition that is fundamentally being pursued by capitalism while at the same time seeking the value of society and community. Already existing in Korea's traditional roots, such objectives have become the bases of the Shinbaram culture, characterized by the South Koreans' spontaneity, creativity, and optimism. In the process of a community's gradual improvement of its rules and procedures, the trust among the community members increases, and the "social capital" that guarantees the successful control of shared resources can be established (Ostrom, 2010). This basic ideal can help reduce the gap between large enterprises and SMEs, alleviating the South Koreans' victim mentality in the face of competition and the open-door policy, and creating crenovative corporate competitiveness. The win-win growth policy emerged for the purpose of addressing the polarization and imbalance structure resulting from the evolution of 21st-century capitalism. It simultaneously pursues efficiency and fairness on one hand and economic and community values on the other, and aims to foster efficient interaction between the market and the government. This policy, however, is also evolving. The win-win growth policy can be considered an extension of the win-win cooperation that the past 'Participatory Government' promoted at the enterprise management level to the level of systems and culture. Also, the ecosystemic development agendum that has recently emerged is a further extension that has been presented as a national ideal of "a new development model that promotes the co-advancement of environmental conservation, growth, economic development, social integration, and national and individual development."
Introduction As consumers' purchase behavior change into a rational and practical direction, the discount store industry came to have keen competition along with rapid external growth. Therefore as a solution, distribution businesses are concentrating on developing PB(Private Brand) which can realize differentiation and profitability at the same time. And as improvement in customer loyalty beyond customer satisfaction is effective in surviving in an environment with keen competition, PB is being used as a strategic tool to improve customer loyalty. To improve loyalty among PB users, it is necessary to develop PB by examining properties of a customer group, first of all, quality level perceived by consumers should be met to obtain customer satisfaction and customer trust and consequently induce customer loyalty. To provide results of systematic analysis on relations between antecedents influenced perceived quality and variables affecting customer loyalty, this study proposed a research model based on causal relations verified in prior researches and set 16 hypotheses about relations among 9 theoretical variables. Data was collected from 400 adult customers residing in Seoul and the Metropolitan area and using large scale discount stores, among them, 375 copies were analyzed using SPSS 15.0 and Amos 7.0. The findings of the present study followed as; We ascertained that the higher company reputation, brand reputation, product experience and brand familiarity, the higher perceived quality. The study also examined the higher perceived quality, the higher customer satisfaction, customer trust and customer loyalty. The findings showed that the higher customer satisfaction and customer trust, the higher customer loyalty. As for moderating effects between PB and NB in terms of influences of perceived quality factors on perceived quality, we can ascertain that PB was higher than NB in the influences of company reputation on perceived quality while NB was higher than PB in the influences of brand reputation and brand familiarity on perceived quality. These results of empirical analysis will be useful for those concerned to do marketing activities based on a clearer understanding of antecedents and consecutive factors influenced perceived quality. At last, discussions about academical and managerial implications in these results, we suggested the limitations of this study and the future research directions. Research Model and Hypotheses Test After analyzing if antecedent variables having influence on perceived quality shows any difference between PB and NB in terms of their influences on them, the relation between variables that have influence on customer loyalty was determined as Figure 1. We established 16 hypotheses to test and hypotheses are as follows; H1-1: Perceived price has a positive effect on perceived quality. H1-2: It is expected that PB and NB would have different influence in terms of perceived price on perceived quality. H2-1: Company reputation has a positive effect on perceived quality. H2-2: It is expected that PB and NB would have different influence in terms of company reputation on perceived quality. H3-1: Brand reputation has a positive effect on perceived quality. H3-2: It is expected that PB and NB would have different influence in terms of brand reputation on perceived quality. H4-1: Product experience has a positive effect on perceived quality. H4-2: It is expected that PB and NB would have different influence in terms of product experience on perceived quality. H5-1: Brand familiarity has a positive effect on perceived quality. H5-2: It is expected that PB and NB would have different influence in terms of brand familiarity on perceived quality. H6: Perceived quality has a positive effect on customer satisfaction. H7: Perceived quality has a positive effect on customer trust. H8: Perceived quality has a positive effect on customer loyalty. H9: Customer satisfaction has a positive effect on customer trust. H10: Customer satisfaction has a positive effect on customer loyalty. H11: Customer trust has a positive effect on customer loyalty. Results from analyzing main effects of research model is shown as