• Journal of Korean Academy of Nursing
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• v.23 no.3
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• pp.467-486
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• 1993

Today, more chronically ill and handicapped people are being cared for at home by a family member caregiver. The task of caring for a family momber may mean that the caregiver has less time and money and more work which may result in increased fatigue and symptoms of illness. This study was done to examine the well-being of family caregivers. Fifty three family caregivers were interviewed. Concepts were measured using existing tools and included : Burden(25 item 5 point scale), Social sup-port (21 item 7 point scale), Health status defined by a symptom checklist(48 item S point scale), and Well -being defined by a quality of life scale (14 item 7 point scale) and caregiving activities. Data collection was done by interview and Q-sort. Social support and well - being were positively correlated as were symptoms and burden. Symptoms and burden were negatively correlated with social support and well-being. Items on the quality of life scale had a mean score range from 3.09 to 4.96. Quality of life related to income was lowest (3.09) but the desire to use more money for the patient was rated 2.90 on the burden scale where the item means ranged from 0.73 to 3.55. The high mean of 3.55 was for obligation to give care and the low 0.73 was (or not feeling that this was helping the patient. Mean scores for symptoms ranged from 0.26 to 2.15 with the 2.15 being for “worry about all the things that have to be done.” Over half of the patients were dependent for help with some activities of daily living. The caregivers reported doing an average of 3.40 out of five patient care activities including bathing (77.4%), shampooing (67.9%), and washing face and hands (49.1％), and 3.74 out of seven home maintenance activities including laundry (98.1％), cooking (83.0%), and arranging bed-ding(75.5%). The caregivers reported their spouse as one of the main sources of social support, including in times of loneliness and anger The mean score for loneliness as burden was 2.15 and ranked fourth and 31 (58.5％) of the sample reported being lonely recently and not being satisfied with the support received. Similarly anger caused by the patient was given a mean score of 2.13, and anger was reported to have been present recently by 38 (71.7%) of the sample and satis-faction with the support given was low. Having someone to help deal with anger ranked twelfth out of 21 items on the social support scale and had a mean score of 3.98 (range 3.49 to 5.98). Spouses were reported as a major source of social support but the fact that 50% of the caregivers were caring for a spouse, may account for the quality of this source of social support having been affected. These caregivers faced the same problems as others at the same stage of life. but because of the situation, there was a strain on their resources, particularly financial and social. In conclusion it was found that burden is correlated negatively to quality of life and positively to symptoms, but in this sample, symptoms and bur-den were scored relatively low. Does this indicate that the caregivers accept caregiving as part of their destiny and accept the quality of their lives with burden and symptoms just being a part of caregiving\ulcorner Does the correlation between the bur-den and symptoms indicate they are a measure of the same phenomenon or that the sample was of a more mobile, less burdened group of caregivers\ulcorner Quality of life was the one variable that was significant in explaining the varience on burden. Further study is needed to validate the conclusions found in this study but they indicate a need for nurses to ap-proach these caregivers with a plan tailored to each individual situation and to give consideration to interventions directed at improving quality of life and expanding social support networks for those caring for spouses.

• Journal of Internet Computing and Services
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• v.24 no.4
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• pp.127-135
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• 2023

This research paper explores the application of FinBERT, a variational BERT-based model pre-trained on financial domain, for sentiment analysis in the financial domain while focusing on the process of identifying suitable training data and hyperparameters. Our goal is to offer a comprehensive guide on effectively utilizing the FinBERT model for accurate sentiment analysis by employing various datasets and fine-tuning hyperparameters. We outline the architecture and workflow of the proposed approach for fine-tuning the FinBERT model in this study, emphasizing the performance of various datasets and hyperparameters for sentiment analysis tasks. Additionally, we verify the reliability of GPT-3 as a suitable annotator by using it for sentiment labeling tasks. Our results show that the fine-tuned FinBERT model excels across a range of datasets and that the optimal combination is a learning rate of 5e-5 and a batch size of 64, which perform consistently well across all datasets. Furthermore, based on the significant performance improvement of the FinBERT model with our Twitter data in general domain compared to our news data in general domain, we also express uncertainty about the model being further pre-trained only on financial news data. We simplify the complex process of determining the optimal approach to the FinBERT model and provide guidelines for selecting additional training datasets and hyperparameters within the fine-tuning process of financial sentiment analysis models.

• The Journal of Asian Finance, Economics and Business
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• v.3 no.3
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• pp.39-55
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• 2016

The rapid expansion of China's trade surplus since the mid-eighties and picking up until the onset of 2008-09 global financial crisis has been a key development in the world economy. While growing trade surplus of China has been viewed with cynicism borne out of an undervalued Yuan and for having being a member of WTO since 2001, many others argue that China's trade surplus reflect changes in China's economic and trade structure and associated shifts in its role within regional and global production chains. We address this issue by analyzing: (i) China's growing and changing trade structure as well as changing structure of trade surplus with the rest of the world, USA, Europe, Japan and rest of Asia, (ii) China's revealed comparative advantage (RCA) with the rest of the world, and (iii) how China's trade policies resulted into a shift in China's trade structure. We find that, not only China has made significant inroads in the world trade since its admission to WTO, but also there has been a noticeable shift in China's trade structure with specialization in high-end technology industries wherein China's exports aided by a well calibrated FDI policy.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.49-62
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• 2023

There has been a rapid increase in the creation and alteration of new malware samples which is a huge financial risk for many organizations. There is a huge demand for improvement in classification and detection mechanisms available today, as some of the old strategies like classification using mac learning algorithms were proved to be useful but cannot perform well in the scalable auto feature extraction scenario. To overcome this there must be a mechanism to automatically analyze malware based on the automatic feature extraction process. For this purpose, the dynamic analysis of real malware executable files has been done to extract useful features like API call sequence and opcode sequence. The use of different hashing techniques has been analyzed to further generate images and convert them into image representable form which will allow us to use more advanced classification approaches to classify huge amounts of images using deep learning approaches. The use of deep learning algorithms like convolutional neural networks enables the classification of malware by converting it into images. These images when fed into the CNN after being converted into the grayscale image will perform comparatively well in case of dynamic changes in malware code as image samples will be changed by few pixels when classified based on a greyscale image. In this work, we used VGG-16 architecture of CNN for experimentation.

• Journal of Korean Academy of Nursing
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• v.4 no.1
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• pp.64-80
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• 1974

The social understanding of the rehabilitation and welfare work for the handicapped children is nearly devoid in Korea. Especially the medical rehabilitation or nursing for them is left ign. ored as well as the literature preferential to this study is wanted. For the purpose of conducting the study of this thesis Sam Yook Children's Rehabilitation Center, Inc. and Crippled Children's Center, Yonsei University Medical center sampled out; covering three months from Aug. 1 to Oct. 30, 1973, the problems of children's rehabilitation, the characteristics of the children, the medical rehabilitation, nursing, education, and vocational rehabilitation were interviewed and widely grasped. This thesis aimed at developing the rehabilitation, the professional nursing and especially aimed at contributing to the improvement of welfare for the handicapped by analysing, observing the result of this study. The result is as follows: 1. Sam Yook Children's Rehabilitation Center, Inc. has, since its establishment, contributed to the advancement of the crippled children and its purpose consists in implementing services, medical rehabilitation, nursing, education and vocational training. Crippled Children's Center, Yonsei University Medical Center is, being based on Christianity, aiming at the medical treatment and education for the crippled children. 2. All of the children admitted in the children's rehabilitation centers are the crippled children. The ratio of boys io girls is three to two. Boys are more than girls. Orphan formed 55.2 per sent of them. About 60 per cent of them is receiving medical attention free of charge. But there is no orphan in Crippled Children's Center, Yonsei University Medical Center. 3. 15.7 per cent of them have received the previous medical attention before their admitting in the centers; in Sam Yook Children's Rehabilitation Center 8.6 percent, Crippled Children's Center, Yonsei University Medical Center 50 percent; there is remarkable difference between the two. 4. On the standpoint of period of being in the centers, the children who have been over three years in Sam Yook Children's Rehabilitation Center formed 48.7 per cent; in Crippled Children's Center, Yonsei University Medical Center 2.6 percent; there is also considerable difference between the two; they couldn't discharge from the centers owing to the economic conditions and being orphan. 5. Among the diagnosis of the crippled children, poliomyelitis formed highest 51.7 percent of them ; cerebral palsy formed 30 percent secondly in order. Environmental factors (67.8 percent formed about three times of congenital factors (23.7 percent). 6. The children who are capable of doing independently activity of daily living formed 87.9 percent; 73.2 percent can walk about 300 m by wearing brace and so on. The ratio of wearing brace or leg prosthesis formed 47.4 per cent: crutch 44.3 per cent. The medical rehabilitation service and education for the crippled, are comparatively well carried out. But it is desired to improve and cultivate the vocational training, vocational guidance and special nursing to the insufficiency of their implementation. In the tendency that the rehabilitation and welfare for the handicapped are today emphasized, International Handicapped Rehabilitation Association, declaring that 1970s are the decade of rehabilitation, urged to improve positively the policy of rehabilitation and welfare for the handicapped. But here in korea the handicapped, being the object of social prejudice, ignorance, disdain lives in obscurity. Therefore the government or the community should draw up countermeasure on social under-standing, financial support, and rehabilitation services for the handicapped as well as should endeavor to make them participate in social activities as the productive total- person though they are physically imperfect.

• Journal of Family Resource Management and Policy Review
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• v.12 no.2
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• pp.57-72
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• 2008

The propose of this study is to examine the economic stresses and coping strategies for elderly households depending on the variables of background, and to analyze the relations between these factors. Elderly households which satisfied the following criteria were recruited for participation: (a) reside in Seoul, or in the metropolitan area (b) live apart from their adult children after retirement and (c) ages over sixty. From September 20th, 2006 to November 30th, 2006, 296 were used for this research. First, the mean score of the economic stress level of elderly households was 2.87 out of 5, and the stress levels of income expenditure and asset debt were intermediate. The mean score of the economic coping strategy was 3.17 out of 5. In order to overcome economic stress, elderly households utilized reducing their expenditure, financial management, and re-employment. The level of elderly households' life satisfaction was 3.29 of 5 and the depression was 3.17 of 5. Second, there was a difference in accordance with the objective economic variables and the degree of the economic stress after the review of the variables of the elderly households and the coping strategy due to economic stress. The result shows that the households which had a low economic status and high economic stress from the objective viewpoint participated in more economic activities. Their reactions were to decrease the overall expenditure through reducing the expenditure rather than to manage the asset effectively through re-employment or to inaugurating a business. Third, I analyzed situational factors, economic stress, and economic coping strategy in order to compare relative contributors to psychological well-being through using regression. At the third phase in the process of analysis, the socio-psychological factors appeared to be significant factors contributing to psychological well-being. Regarding the stress caused by income expenditure increased, when elderly households were more concerned about reducing expenditure and re-employment, their feelings of depression increased.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.10
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• pp.131-142
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• 2018

Being dealt in Alfred Weber's Theory of the location of Industries, a lot of economic benefits can be obtained through aggregation and clustering of industrial facilities, which derived to the development of industrial complexes in Korea. However, with the IMF economic crisis as well as various institutional changes, the framework of aggregation and clustering of industries is broken, which led to individual developments that took place without any consideration of surrounding industries. For reformation of these condition of industrial complexes, national government-led regeneration projects are being carried out currently. However, national government-led projects mainly focus on profitable projects such as officetel and hotel that are irrelevant to exist composition of industrial complexes which is usually manufacturing base industries and are unable to solve the fundamental problems of industrial complexes. Thus, a necessity of industry clustering is deduced through case analysis of actual private-led manufacturing industry cluster with governance and analysis of benefits on financial, spatial and environmental aspects. In addition, implications on the necessity follow base on factorial analysis on the benefit of clustering development than individual development as well as analysis on the measures taken for successful clustering.

• 한국노년학
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• v.38 no.2
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• pp.271-290
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• 2018

The study aims to analyze the effects of socio-economic deprivation on suicidal ideation. The analysis data were used as a guide for Korea Welfare Panel Study 9. the frequency analysis, mean difference analysis, correlation analysis, and logistic regression were performed by SPSS programs. The results of analysis are as follows. First, The results of frequency analysis by deprivation type showed a high frequency of deprivation in the following order. Experience of not receiving a public pension, experience of being able to work but unemployed, experience of not being able to eat a balanced diet due to financial difficulties, and experience where you had nothing to eat but no more money to buy. Second, the average difference analysis shows that when a person does not have a spouse, the lower the academic background and the income level, the higher the likelihood of suicide. Third, regression analysis shows that the following deprivation patterns have a statistically significant effect on older adults' thoughts of suicide. Experience in which the respondents or their family could not go to hospital because they had no money, experience that move house because is back rent more than 2 months or can not pay rent, experience that they could not afford to buy food and eat well-balanced meals, experience of failing to pay your bills on time, experience of being able to work but not having a job, and experience in which financial difficulties left them short of food and no money to live. Based on such research results, some policy measures, such as the expanding management of medical care benefits cases, the improvement of elderly housing, residential conditions and the diet survey for the elderly, and the expansion of measures to support elderly people's tax rates, were proposed.

• Proceedings of the Korea Database Society Conference
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• 1999.06a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support fer multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To date, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques' results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.175-186
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• 1999

Detecting the features of significant patterns from their own historical data is so much crucial to good performance specially in time-series forecasting. Recently, a new data filtering method (or multi-scale decomposition) such as wavelet analysis is considered more useful for handling the time-series that contain strong quasi-cyclical components than other methods. The reason is that wavelet analysis theoretically makes much better local information according to different time intervals from the filtered data. Wavelets can process information effectively at different scales. This implies inherent support for multiresolution analysis, which correlates with time series that exhibit self-similar behavior across different time scales. The specific local properties of wavelets can for example be particularly useful to describe signals with sharp spiky, discontinuous or fractal structure in financial markets based on chaos theory and also allows the removal of noise-dependent high frequencies, while conserving the signal bearing high frequency terms of the signal. To data, the existing studies related to wavelet analysis are increasingly being applied to many different fields. In this study, we focus on several wavelet thresholding criteria or techniques to support multi-signal decomposition methods for financial time series forecasting and apply to forecast Korean Won / U.S. Dollar currency market as a case study. One of the most important problems that has to be solved with the application of the filtering is the correct choice of the filter types and the filter parameters. If the threshold is too small or too large then the wavelet shrinkage estimator will tend to overfit or underfit the data. It is often selected arbitrarily or by adopting a certain theoretical or statistical criteria. Recently, new and versatile techniques have been introduced related to that problem. Our study is to analyze thresholding or filtering methods based on wavelet analysis that use multi-signal decomposition algorithms within the neural network architectures specially in complex financial markets. Secondly, through the comparison with different filtering techniques results we introduce the present different filtering criteria of wavelet analysis to support the neural network learning optimization and analyze the critical issues related to the optimal filter design problems in wavelet analysis. That is, those issues include finding the optimal filter parameter to extract significant input features for the forecasting model. Finally, from existing theory or experimental viewpoint concerning the criteria of wavelets thresholding parameters we propose the design of the optimal wavelet for representing a given signal useful in forecasting models, specially a well known neural network models.