• Journal of muscle and joint health
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• v.2 no.1
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• pp.17-40
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• 1995

Rheumatoid arthritis is the one of the chronic diseases, one of its major symptoms is a chronic pain. Despite developing medical treatment and surgical techniques, it is suggested that to control the pain is the goal of the treatment. But pain is an inner experience and even those closest to the patient cannot truly observe its progress or share in its suffering. The National Academy of Sciences Institute of Medicine's report on Pain and Disability concluded that there is no objective measure of pain-(exactly) no pain thermometer-nor can there ever be one, because the experience of pain is inseparable from personal perception and social influence such as culture. To explore chronic pain experience is to understand the process and property of the patient's perception of pain through the response to pain, the coping with pain, and the adaptation to pain. Therefore a qualitative study was conducted in order to gain an understanding of pain experience of patients with RA in korea. I used naturalistic inquiry as a research methodology, which had 5 axioms, the first is that realities are multiple, constructed, and holistic, the second is that knower and known are interactive, inseparable, the third is only time and context bound working hypotheses(idiographic statements) are possible, the forth is all entities are in a state of mutual simultaneous shaping, so that it is impossible to distinguish causes from effects and the last is that inquiry is value-bound. Purposive sampling was conducted as a sampling. 20 subjects who experienced pain over 10 years, lived in middle-sized city and big city in Korea, and 17 women and 3 men. The subject's age was from 32 to 62 (average 48.8), all were married, living with their spouse and children, except two-one divorced and the other widow before they became ill. I collected data using In depth structured interview. I had interviews two or three times with each subject, and the interviews were conducted at each subject's home. Each interview lasted about two hours an average. A recording was taken with the consent of the subject. I used inductive data analysis-such as unitizing and categorizing. unitizing is a process of coding, whereby raw data are systematically transformed and aggregated into units. Categorizing is a process wherby previously unitized data are organized into categories that provide descriptive or inferential information about the context or setting from which the units were derived. This process is used constant comparative method. The pain controlling process is composed of behavior of pain control. The behaviors of pain control are rearranging of ADL, hiddening role conflict, balancing treatment, and changing social relation. Rearranging of ADL includes diet management, sleep management, and the adjustment of daily life activities. The subjects try to rearrange their daily activities by modified style of motions, rearranging time span & range of activities, using auxillary facilities, and getting help in order to keep on the pace of daily life. Hiddening role conflict means to reduce conflicts between sick role and their role as a family member. In this process, the subjects use two modes, one is to control the pain complaints, and the other is to internalize the value which is to stay home is good for caring her children and being a good mother. To control pain complaints is done by 'enduring', 'understanding' the other family members, or making them undersood in order to reduce pain. Balancing treatment is composed of two aspects. One is to keep the pain within the endurable level, the other is to keep in touch with medical personnel in order to get the information of treatment and emotional support. Changing social relation is made by information seeking and sharing, formation of mutual support relation, and finally simplification of social relationships. The subjects simplify their social relationships by refraining from relations with someone who makes them physically and psychologically strained. In particular the subjects are apt to avoid contact with in-laws, and the change of relation to in-laws results in lessening the family boundary. In the course of this process, they confront the crisis of family confict result in family dissolution. This crisis is related to the threat of self-existence. Findings from this study contribute to understanding the chronic pain experience. To advance this study, we should compare this result with other cases in different cultural contexts. I think to interpret these results, korean cultural background should be considered. Especially the different family concept, more broader family members and kinship network, and the traditional medical knowledge influences patients' behavior.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.10
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• pp.591-599
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• 2016

Among the employer-supported subscribers to the National Health Insurance Service, 6,797 people with mild disabilities with western ages of 20 and up and who received health checkups were investigated. Of these 6,797 people, 3,186 and 3,611 received health checkups in 2009 and 2013, respectively. Those people who were diagnosed with physical handicaps, brain lesions, visual impairment, hearing impairment, intellectual disabilities, mental disorders, kidney disorders or other disorders according to the classification standard for people with disabilities were classified into disability groups of the 3rd through 6th degrees. The purpose of this study was to examine the dangerous influence of obesity of people with mild disabilities on their hyperglycemia, hypertension and high cholesterol. The items measured in this study were abdominal obesity, body mass index, fasting glucose, total cholesterol, systolic blood pressure and diastolic blood pressure. To look for connections between the obesity level and at-risk groups for each disease, cross tabulation and multinomial logistic regression analyses were utilized. Higher levels of abdominal obesity and BMI were found among those who were male, were younger and had higher incomes. The risks of abdominal obesity and BMI were higher in the abnormal groups for each disease. In 2009, the obesity group whose BMI was higher had a 1.51-fold higher risk of hypertension than the normal group. The abdominal obesity group had a 1.59-fold higher risk of high cholesterol, a 1.26-fold higher risk of hypertension and a 1.54-fold higher risk of hyperglycemia than the normal group. In 2013, the obesity group whose BMI was higher had a 1.72-fold higher risk of high cholesterol and a 1.43-fold higher risk of hypertension than the normal group. Those with abdominal obesity had a 1.59-fold higher risk of hyperglycemia than the normal subjects. As the risk of obesity was higher in those with disabilities than in those without disabilities, the former should be encouraged to undergo health checkups on a regular basis, and the coverage of the health checkups should be extended to keep track of their illness. In addition, appropriate education and concern are both required to prevent obesity.

• Journal of Christian Education in Korea
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• v.67
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• pp.157-194
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• 2021

The issue of AI's ethical consciousness has been constantly on the rise. AI learns and imitates everything behavior human beings do, just like a child. Therefore, the ethical consciousness we currently demand from AI is first the ethical consciousness required of humans, and at the center of it is the dignity of humans. Thus, this study analyzed human identity and its problems according to the development of AI technology, apologized the theological premises and characteristics of human dignity, and sought the direction of human dignity education as follows. First, this study discussed the development of AI and its relation to human beings. The development of AI's technology has led to the sharing of "reason or intelligence" with machines called AI which have been restricted to the exclusive property of mankind. This raised the question of the superior humanity which humans would be remained to be distinguished from AI machines. Second, this study discussed transhumanism and human identity. Transhumanism has been argued for the combination of AI machines and humans in order to improve inefficient human intelligence and human capabilities. However, the combination of AI machines with humans raised the issue of human identity. In the AI era, human identity is to believe thoughts that God had when he built us. Third, this study apologized theological premise and characteristic about human dignity. Human dignity has become a key concept of the constitution and international human rights treaties around the world. Nonetheless, declarative conviction that human is dignified is difficult to be understanded without Christian theological premise. Theological premise of human dignity lies on the fact that human is dignified feature being granted life by Heavenly Father. This feature lies on longing for "Goodness" and "eternality", pursuit of beauty, a happy being in relationship with others. Fourth, this study presented the direction of human dignity education. The direction of human dignity education has to awaken what is identity of human and how human beings were created and how much they are precious. Furthermore, it lead human to ponder consciously and accept the highest value of what human beings are, how they were created, and how precious they are. That is about educating human identity, and its core is that regardless of the circumstances - the wealth gap, knowledge level, skin color, gender, age, disability, etc. - all people are in God's image and for the glory of God, thereby being very important to God.

• 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.