• Tuberculosis and Respiratory Diseases
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• v.45 no.1
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• pp.153-168
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• 1998

Background: The existing data indicate that obstructive sleep apnea syndrome contributes to the development of cardiovascular dysfunction such as systemic hypertension and cardiac arrhythmias, and the cardiovascular dysfunction has a major effect on high long-term mortality rate in obstructive sleep apnea syndrome patients. To a large extent the various studies have helped to clarify the pathophysiology of obstructive sleep apnea, but many basic questions still remain unanswered. Methods: In this study, the influence of obstructive sleep apnea on systemic blood pressure, cardiac rhythm and urinary catecholamines concentration was evaluated. Over-night polysomnography, 24-hour ambulatory blood pressure and ECG monitoring, and measurement of urinary catecholamines, norepinephrine (UNE) and epinephrine (UEP), during waking and sleep were undertaken in obstructive sleep apnea syndrome patients group (OSAS, n=29) and control group (Control, n=25). Results: 1) In OSAS and Control, UNE and UEP concentrations during sleep were significantly lower than during waking (P<0.01). In UNE concentrations during sleep, OSAS showed higher levels compare to Control (P<0.05). 2) In OSAS, there was a increasing tendency of the number of non-dipper of nocturnal blood pressure compare to Control (P=0.089). 3) In both group (n=54), mean systolic blood pressure during waking and sleep showed significant correlation with polysomnographic data including apnea index (AI), apnea-hypopnea index (AHI), arterial oxygen saturation nadir ($SaO_2$ nadir) and degree of oxygen desaturation (DOD). And UNE concentrations during sleep were correlated with AI, AHI, $SaO_2$ nadir, DOD and mean diastolic blood pressure during sleep. 4) In OSAS with AI>20 (n==14), there was a significant difference of heart rates before, during and after apneic events (P<0.01), and these changes of heart rates were correlated with the duration of apnea (P<0.01). The difference of heart rates between apneic and postapneic period (${\Delta}HR$) was significantly correlated with the difference of arterial oxygen saturation between before and after apneic event (${\Delta}SaO_2$) (r=0.223, P<0.001). 5) There was no significant difference in the incidence of cardiac arrhythmias between OSAS and Control In Control, the incidence of ventricular ectopy during sleep was significantly lower than during waking. But in OSAS, there was no difference between during waking and sleep. Conclusion : These results suggested that recurrent hypoxia and arousals from sleep in patients with obstructive sleep apnea syndrome may increase sympathetic nervous system activity, and recurrent hypoxia and increased sympathetic nervous system activity could contribute to the development of cardiovascular dysfunction including the changes of systemic blood pressure and cardiac function.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.7
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• pp.1018-1027
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• 2005

The purpose of this study was to investigate nutrients intake and health-related behaviors in elderly people residing in Gyeongsan-si, Gyeongbuk who have no problem in daily living. Information on general characteristics of the elderly, health-related behaviors and dietary habits were obtained by interview based on questionnare. Dietary nutrients intake data were obtained through the 24 recall method. The subject group of this study was composed of 113 males and 112 females, the average age being $73.1\pm6.06$ years old. In health related factors, $76.9\%$ of subjects exercised regularly. The rates of alcohol drinking and smoking showed to be $38.2\%\;and\;22.2\%$, respectively. There were many elderly with neuralgia, hypertension, and gastrointestinal disease, especially female were worse. Average heights of the subjects were lower than the standard established in the Korean Recommended Dietary Allowances, and average weights were similar to the standards. The mean BMI and WHR were 24.8 (male 23.7, female 25.7) and 0.92 (male 0.92, female 0.89), respectively. Most of the subjects had a regular meal pattern comsuming three meals a day, and many elderly, especially more than $79.5\%$ of female, prepared the meals for themselves. Mean daily energy intakes and RDA percentage of energy intakes of the male and female subjects were estimated as 1426.9kcal $(79.3\%)$ and 1381.3 kcal $(86.3\%)$, respectively. Mean daily intakes of nutrients were estimatied as 48.1g for protein, 411.3mg for calcium, 8.05mg for iron, 541.8 R.E. for vitamin A, 0.84mg for vitamin $B_1$, and 0.79mg for vitamin $B_2$. Most nutrients except protein, clacium, iron and vitamin $B_2$ were consumed over $75\%$ of the RDA. Female elderly showed significant lower intakes (p<0.05) for most of the nutrients except calcium, phosphorus and vitamin ethan the elderly male.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used