• Journal of Chest Surgery
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• v.38 no.1 s.246
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• pp.29-37
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• 2005

This study was prospectively designed to determine the physiologic effects of normothermic CPB and to compare its influences with hypothermic CPB. Material and Method: Thirty-six adult patients scheduled for el­ective cardiac surgery were randomly assigned to moderate hypothermic (hypothermic group nasopharyngeal tem­perature $26\~28^{\circ}C,\;n=18)$ ornormothermic (normothermic group, nasopharyngeal temperature > $35.5^{\circ}C\;n=18)$ CPB. Arterial blood samples were taken before CPB (Pre-CPB), 10 minutes after the start of CPB (CPB-10), and imme­diately after CPB stop (CPB-off) for determining total leukocyte counts, neuron-specific enolase (NSE), interleukin-6 (IL-6), endothelin-1 (ET-1), cortisol, troponin I (TNI), aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine, blood urea nitrogen (BUN), and the pulmonary index $(Pi,\;PaO_{2}/FiO_{2}),$Other parameters such as urine output, mechanical ventilating period, ICU-staying period, postoperative complications and hospitalized days were also evaluated. Result: Total leukocyte counts, increased rate in NSE, in IL-6 and in cortisol at CPB-10 and CPB-off were significantly higher in normothermic group than in hyphothermic group. Urine output during CPB was lower in normothermic group than in hyphothermic group. The duration of mechanical ventilation, ICU-stay, and hospitalization were longer in normothermic group than in hyphothermic group. Conclusion: These findings sug­gested that normothermic CPB caused higher inflammatory and stress responses than hypothermic CPB during car­diac surgery using cold crystalloid cardioplegia. However, further studies with large number of cases should be carried out to validate this hypothesis.

• Journal of Biosystems Engineering
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• v.6 no.2
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• pp.9-19
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• 1982

The aim of this study was to improve the startability of the diesel engine at low temperature. The specific objective was to determine the optimum type of precombustion chamber. The eight different types of precombustion chamber and two different types of the cylinder head were designed and tested by $2^7$ factorial experiments with four replications. The lowest starting temperature for first operation, the maximum output, and the specific fuel consumption at full load and overload were checked and analyzed. The results of the study are summarized as follows; 1. The lowest starting temperature was lowered as much as $2.4^{\circ}C$ and the maximum output was increased as much as 0.3 ps with respect to the difference in the relative angle of the main passageway against the piston head from 20 degree to 18 degree. 2. The lowest starting temperature and the maximum out-put were lowered as much as $3.3^{\circ}C$ and 0.3 ps respectively with respect to the difference in the angle of the cylinder head groove from 20 degree to 18 degree. 3. The lowest starting temperature and the maximum out put were lowered as much as $2^{\circ}C$ and 0.2 ps respectively with respect to the difference in the length of the precombustion chamber from 17.5 mm to 15.5mm. 4. There was no significant difference in the startability but the maximum output was increased as much as 0.2 ps with respect to the difference in the diameter of the main passageway from 4.8mm to 4.5mm. 5. The lowest starting temperature was obtained under the condition at 47 degree in the angle of the main passageway and at 18 degree in the angle of the cylinder head groove. The maximum output and the minimum specific fuel consumption was obtained under the condition at 4.5mm in the diameter of the main passageway and at 17.5mm in the length of the precombustion chamber. 6. The angle of the cylinder head groove and the main passageway appeared to the major factors affecting the startability significantly. The interaction between the diameter of the main pass ageway and the length of the precombustion chamber had an significant influence on the maximum output. So it would be recommended to study further on the interaction between two factors mentioned above by expanding their levels. 7. The optimum condition suggested by this study could lower the starting temperature by $6^{\circ}C$ compared to the conventional precombustion chambers.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.305-316
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• 2011

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

• Journal of the Korean Home Economics Association
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• v.5 no.1
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• pp.733-739
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• 1966

A 371 agricultural households from 26 different communities in South Korea was subjected on a study of food taboos in January of 1966. To the pregnant women, those to whom a high protein diet is particurally important, as many as 14 different kinds of foods, mostly portein rich foods, were avoided to eat. It is believed that if duck is eaten while pregnant her baby may walk like a duck in later life. Some mother have a strong aversion to the rabbit meat that her unborn baby must be a harelip. It is feared to eat chicken, shark or carp by the pregnant mother for her baby may get a gooseflesh appearance, or fish scale-like skin in later life. It is thought that if mother eats soup made of meat borns, especially chicken bones, a disfigured baby may be born. Some area informed that if mother eats crab meat her future baby will always bubble. To the child-bearing mothers 13 different kinds of foods were avoided to eat. Some believe that if raddish kimchi, soybean curd, squash are eaten while dilivery that mother may get dental decay or to lose all her teeth. Other think that highly spiced raddish kimchi cause delivery difficult. To the lactating mothers 7 different items of foods were not recommended to eat. It is a common belief that eating green vegetables, especially fresh lettuce, are restricted that her baby may stool greenish. It is said that eating ginsen-chicken soup, or ginsen tea during lactating reduces breast milk secretion. To the weaning babies 7 different kinds of foods were prohibited to fee. Eggs are not eaten because mothers think her babies will start to talk very late. Eight different items of foods in cases of gastro-intestinal diseases, 5 items for liver disease, 7 items for high blood pressure as well as for paralysis were respectively restricted. It is said that meats including pork, beef, and chicken are neither desirable for the patients of high blood pressure nor those of paralysis. To the measles children 10 varieties of foods were restricted. Especially soybean products and meats were not encouraged to use for avoiding asecond attack of measles. For the common cold 8 different kinds of foods were aversed and men think that eating of soup of undria delays a recovery. For the tuberculosis 4 kinds of foods were prohibited to eat. It is said that wine, red pepper and ginsen will stimulate lung bleeding. Many mothers had a strong aversion to fermented shrimp and fish in case of style. and 5 different items of foods were restricted. In case of menstration not so many foods were restricted as other cases, but meat soup is not eaten in this condition in some areas. Majority of food taboos in Korean villages are neither based on tribal nor religious factors. But no one knows how, since what ages, from where, these food taboos have been transmitted and spread over the country. This survey found a great variety of food taboos, aversions, traditional beliefs and prohibitions latent unknown reseasons, or non-scientific conceptions, or completely different ideas from the modern medical aspect, or somewhat fallacious and superstitious beliefs. For the vascular disease contrasting approach were found between modern the oritical therapy and popular remedy among the rural populations who largely depend on the eastern medication. Further scientific study on either side should be done to lead the patient proper way. Many restricted foods such as rabbit, duck, chicken and fish are best resources of protein rich foods which are available in the village. Emphasis should be laid upon breaking down fallacious and supersititious food taboos through the extended nutrition education activities in order to improve food habit and good eating pattern for healthier and stronger generations of Korea.

• The Korean Journal of Pain
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• v.2 no.2
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• pp.155-166
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• 1989

Bonica defined, that reflex sympathetic dystrophy (RSD) may develop pain, vasomotor abnoramalities, delayed functional recovery, and dystrophic changes on an affected area without major neurologic injury following trauma, surgery or one of several diseased states. This 45 year old male patient had been crushed on his left shoulder by a heavily laden rear car, during his job street cleaning about 10 years ago (1978). At first the pain was localizea only to the site of injury, but with time, it spreaded from the shoulder to the elbow and hand, with swelling. X-ray studies in the local clinic, showed no bone abnormalities of the affected site. During about 10 years following the injury, the had recieved several types of treatments such as nonsteroidal analgesics, steroid injections into the glenoidal cavity (10 times), physical therapy, some oriental herb medicines, and acupuncture over a period of 1~3 months annually. His shoulder pain and it's joint dysfunction persisted with recurrent paroxysmal aggrevation because of being mismanaged or neglected for a sufficiently long period these fore permiting progression of the sympathetic imbalance. On July 14 1988 when he visited our clinic. He complained of burning, aching and had a hyperpathic response or hyperesthesia in touch from the shoulder girdle to the elbow and the hand. Also the skin of the affected area was pale, cold, and there was much sweating of the axilla and palm, but no edema. The shoulder girdle was unable to move due to joint pain with marked weakness. We confirmed skin temperatures $5^{\circ}C$ lower than those of the unaffected axilla, elbow and palm of his hand, and his nails were slightly ridged with lateral arching and some were brittle. On X-ray findings of both the shoulder AP & lateral view, the left humerus and joint area showed diffuse post-traumatic osteoporosis and fibrous ankylozing with an osteoarthritis-like appearance. For evaluating the RSD and it's relief of pain, the left cervical sympathetic ganglion was blocked by injecting 0.5% bupivacaine 5 ml with normal saline 5 ml (=SGB). After 15 minutes following the SGB, the clinical efficacy of the block by the patients subjective score of pain intensity (=PSSPI), showed a 50% reduction of his shoulder and arm pain, which was burning in quality, and a hyperpathic response against palpation by the examiner. The skin temperatures of the axilla and palm rose to $4{\sim}5^{\circ}C$ more than those before the SGB. He felt that his left face and upper extremity became warmer than before the SGB, and that he had reduced sweating on his axilla and his palm. Horner's sign was also observed on his face and eyes. But his deep shoulder joint pain was not improved. For the control of the remaining shoulder joint pain, after 45 minutes following the SGB, a somatic sensory block was performed by injecting 0.5% bupivacaine 6 ml mixed with salmon calcitonin, $Tridol^{(R)}$, $Polydyn^{(R)}$ and triamcinolone into the fossa of the acromioclavicular joint region. The clinical effect of the somatic block showed an 80% releif of the deep joint pain by the PSSPI of the joint motion. Both blocks, as the above mentioned, were repeated a total of 28 times respectively, during 6 months, except the steroid was used just 3 times from the start. For maintaining the relieved pain level whilst using both blocks, we prescribed a low dose of clonazepam, prazocin, $Etravil^{(R)}$, codeine, etodolac micronized and antacids over 6 months. The result of the treatments were as follows; 1) The burning, aching and hyperpathic condition which accompanied with vaosmotor and pseudomotor dysfunction, disappeared gradually to almost nothing, within 3 weeks from the starting of the blocks every other day. 2) The joint disability of the affected area was improved little by little within 6 months. 3) The post-traumatic osteoporosis, fibrous ankylosis and marginal sclerosis with a narrowed joint, showed not much improvement on the X-ray findings (on April 25, 1989) 10 months later in the follow-up. 4) Now he has returned to his job as a street cleaner.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.6
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• pp.427-446
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• 2008

A review on the papers published in the Korean Journal of Air-Conditioning and Refrigeration Engineering in 2006 has been accomplished. Focus has been put on current status of research in the aspect of heating, cooling, ventilation, sanitation and building environments. The conclusions are as follows. (1) The research trends of fluid engineering have been surveyed as groups of general fluid flow, fluid machinery and piping, etc. New research topics include micro heat exchanger and siphon cooling device using nano-fluid. Traditional CFD and flow visualization methods were still popular and widely used in research and development. Studies about diffusers and compressors were performed in fluid machinery. Characteristics of flow and heat transfer and piping optimization were studied in piping systems. (2) The papers on heat transfer have been categorized into heat transfer characteristics, heat exchangers, heat pipes, and two-phase heat transfer. The topics on heat transfer characteristics in general include thermal transport in a cryo-chamber, a LCD panel, a dryer, and heat generating electronics. Heat exchangers investigated include pin-tube type, plate type, ventilation air-to-air type, and heat transfer enhancing tubes. The research on a reversible loop heat pipe, the influence of NCG charging mass on heat transport capacity, and the chilling start-up characteristics in a heat pipe were reported. In two-phase heat transfer area, the studies on frost growth, ice slurry formation and liquid spray cooling were presented. The studies on the boiling of R-290 and the application of carbon nanotubes to enhance boiling were noticeable in this research area. (3) Many studies on refrigeration and air conditioning systems were presented on the practical issues of the performance and reliability enhancement. The air conditioning system with multi indoor units caught attention in several research works. The issues on the refrigerant charge and the control algorithm were treated. The systems with alternative refrigerants were also studied. Carbon dioxide, hydrocarbons and their mixtures were considered and the heat transfer correlations were proposed. (4) Due to high oil prices, energy consumption have been attentioned in mechanical building systems. Research works have been reviewed in this field by grouping into the research on heat and cold sources, air conditioning and cleaning research, ventilation and fire research including tunnel ventilation, and piping system research. The papers involve the promotion of efficient or effective use of energy, which helps to save energy and results in reduced environmental pollution and operating cost. (5) Studies on indoor air quality took a great portion in the field of building environments. Various other subjects such as indoor thermal comfort were also investigated through computer simulation, case study, and field experiment. Studies on energy include not only optimization study and economic analysis of building equipments but also usability of renewable energy in geothermal and solar systems.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.