• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.21-37
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• 2013

Recently, as the advent of the knowledge-based society, there are more people getting interested in the intellectual property. Especially, the ICT companies leading the high-tech industry are working hard to strive for systematic management of intellectual property. As we know, the patent information represents the intellectual capital of the company. Also now the quantitative analysis on the continuously accumulated patent information becomes possible. The analysis at various levels becomes also possible by utilizing the patent information, ranging from the patent level to the enterprise level, industrial level and country level. Through the patent information, we can identify the technology status and analyze the impact of the performance. We are also able to find out the flow of the knowledge through the network analysis. By that, we can not only identify the changes in technology, but also predict the direction of the future research. In the field using the network analysis there are two important analyses which utilize the patent citation information; citation indicator analysis utilizing the frequency of the citation and network analysis based on the citation relationships. Furthermore, this study analyzes whether there are any impacts between the size of the company and patent citation relationships. 74 S&P 500 registered companies that provide IT and communication services are selected for this study. In order to determine the relationship of patent citation between the companies, the patent citation in 2009 and 2010 is collected and sociomatrices which show the patent citation relationship between the companies are created. In addition, the companies' total assets are collected as an index of company size. The distance between companies is defined as the absolute value of the difference between the total assets. And simple differences are considered to be described as the hierarchy of the company. The QAP Correlation analysis and MR-QAP analysis is carried out by using the distance and hierarchy between companies, and also the sociomatrices that shows the patent citation in 2009 and 2010. Through the result of QAP Correlation analysis, the patent citation relationship between companies in the 2009's company's patent citation network and the 2010's company's patent citation network shows the highest correlation. In addition, positive correlation is shown in the patent citation relationships between companies and the distance between companies. This is because the patent citation relationship is increased when there is a difference of size between companies. Not only that, negative correlation is found through the analysis using the patent citation relationship between companies and the hierarchy between companies. Relatively it is indicated that there is a high evaluation about the patent of the higher tier companies influenced toward the lower tier companies. MR-QAP analysis is carried out as follow. The sociomatrix that is generated by using the year 2010 patent citation relationship is used as the dependent variable. Additionally the 2009's company's patent citation network and the distance and hierarchy networks between the companies are used as the independent variables. This study performed MR-QAP analysis to find the main factors influencing the patent citation relationship between the companies in 2010. The analysis results show that all independent variables have positively influenced the 2010's patent citation relationship between the companies. In particular, the 2009's patent citation relationship between the companies has the most significant impact on the 2010's, which means that there is consecutiveness regarding the patent citation relationships. Through the result of QAP correlation analysis and MR-QAP analysis, the patent citation relationship between companies is affected by the size of the companies. But the most significant impact is the patent citation relationships that had been done in the past. The reason why we need to maintain the patent citation relationship between companies is it might be important in the use of strategic aspect of the companies to look into relationships to share intellectual property between each other, also seen as an important auxiliary of the partner companies to cooperate with.

• Journal of the Society of Cosmetic Scientists of Korea
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• v.30 no.2
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• pp.263-278
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• 2004

Ursolic acid (UA) and Oleanolic acid (ONA), known as urson, micromerol and malol, are pentacyclic triterpenoid compounds which naturally occur in a large number of vegetarian foods, medicinal herbs, and plants. They may occur in their free acid form or as aglycones for triterpenoid saponins, which are comprised of a triterpenoid aglycone, linked to one or more sugar moieties. Therefore UA and ONA are similar in pharmacological activity. Lately scientific research, which led to the identification of UA and ONA, revealed that several pharmacological effects, such as antitumor, hepato-protective, anti-inflammatory, anticarcinogenic, antimicrobial, and anti-hyperlipidemic could be attributed to UA and ONA. Here, we introduced the effect of UA and ONA on acutely barrier disrupted and normal hairless mouse skin. To evaluate the effects of UA and ONA on epidermal permeability barrier recovery, both flanks of 8-12 week-old hairless mice were topically treated with either 0.01-0.1mg/mL UA or 0.1-1mg/mL ONA after tape stripping, and TEWL (transepidermal water loss) was measured. The recovery rate increased in those UA or ONA treated groups (0.1mg/mL UA and 0.5mg/mL ONA) at 6h more than 20% compared to vehicle treated group (p < 0.05). Here, we introduced the effects of UA and ONA on acute barrier disruption and normal epidermal permeability barrier function. For verifying the effects of UA and ONA on normal epidermal barrier, hydration and TEWL were measured for 1 and 3 weeks after UA and ONA applications (2mg/mL per day). We also investigated the features of epidermis and dermis using electron microscopy (EM) and light microscopy (LM). Both samples increased hydration compared to vehicle group from 1 week without TEWL alteration (p < 0.005). EM examination using RuO4 and OsO4 fixation revealed that secretion and numbers of lamellar bodies and complete formation of lipid bilayers were most prominent (ONA=UA > vehicle). LM finding showed that thickness of stratum corneum (SC) was slightly increased and especially epidermal thickening and flattening was observed (UA > ONA > vehicle). We also observed that UA and ONA stimulate epidermal keratinocyte differentiation via PPAR Protein expression of involucrin, loricrin, and filaggrin increased at least 2 and 3 fold in HaCaT cells treated with either ONA (10${\mu}$M) or UA (10${\mu}$M) for 24 h respectively. This result suggested that the UA and ONA can improve epidermal permeability barrier function and induce the epidermal keratinocyte differentiation via PPAR Using Masson-trichrome and elastic fiber staining, we observed collagen thickening and elastic fiber elongation by UA and ONA treatments. In vitro results of collagen and elastin synthesis and elastase inhibitory activity measurements were also confirmed in vivo findings. These data suggested that the effects of UA and ONA related to not only epidermal permeability barrier functions but also dermal collagen and elastic fiber synthesis. Taken together, UA and ONA can be relevant candidates to improve epidermal and dermal functions and pertinent agents for cosmeseutical applications.

• Journal of Plant Biology
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• v.28 no.1
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• pp.69-77
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• 1985

The degree of The degree of The degree of ${Zn}^{2+}$ effect on the photosynthetic electron transport and photophosphorylation activities in barley chloroplasts has been tested.${Zn}^{2+}$treatment was done in the 2 ways. One was that it was added into the chloroplasts suspensions isolated from the plants grown under the normal ${Zn}^{2+}$level (10$^{-6}$ M). The other was that the different concentrations of ${Zn}^{2+}$was applied in each growth medium. Then, it was not added into the chloroplasts suspensions isolated from the plants. PS II activity in both way of the treatments was more severely inhibited than PS I by the increment of ${Zn}^{2+}$ concentration. The photophosphorylation activity measured by pH measurement was gradually decreased with the increase of ${Zn}^{2+}$concentration in both ways, too. However, it was shown that M $n^{2+}$ could be near fully overcome the inhibitory effect of ${Zn}^{2+}$in PS II, and $Mg^{2+}$ could also reduce the Z $n^{2+}$ inhibition in the photophosphorylation. In the low concentrations of $Mg^{2+}$ (3 to 5$\times$10$^{-3}$ M) in the suspension, ${Zn}^{2+}$(2$\times$10$^{-5}$ M) could increase the activity of photophosphorylation. As compares to other cations, Z $n^{2+}$ caused less inhibitory effect on the photophosphorylation activity than Cu, Cd, but more than Pb and Ni. It may be assumed that a complex from reaction of Z $n^{2+}$ and mercaptoethanol was produced and it could reduce the stability of CPI band during SDS-PAGE.effect on the photosynthetic electron transport and photophosphorylation activities in barley chloroplasts has been tested. Z $n^{2+}$ treatment was done in the 2 ways. One was that it was added into the chloroplasts suspensions isolated from the plants grown under the normal Z $n^{2+}$ level (10$^{-6}$ M). The other was that the different concentrations of Z $n^{2+}$ was applied in each growth medium. Then, it was not added into the chloroplasts suspensions isolated from the plants. PS II activity in both way of the treatments was more severely inhibited than PS I by the increment of Z $n^{2+}$ concentration. The photophosphorylation activity measured by pH measurement was gradually decreased with the increase of Z $n^{2+}$ concentration in both ways, too. However, it was shown that M $n^{2+}$ could be near fully overcome the inhibitory effect of Z $n^{2+}$ in PS II, and $Mg^{2+}$ could also reduce the Z $n^{2+}$ inhibition in the photophosphorylation. In the low concentrations of $Mg^{2+}$ (3 to 5$\times$10$^{-3}$ M) in the suspension, Z $n^{2+}$ (2$\times$10$^{-5}$ M) could increase the activity of photophosphorylation. As compares to other cations, Z $n^{2+}$ caused less inhibitory effect on the photophosphorylation activity than Cu, Cd, but more than Pb and Ni. It may be assumed that a complex from reaction of Z $n^{2+}$ and mercaptoethanol was produced and it could reduce the stability of CPI band during SDS-PAGE.effect on the photosynthetic electron transport and photophosphorylation activities in barley chloroplasts has been tested. Z $n^{2+}$ treatment was done in the 2 ways. One was that it was added into the chloroplasts suspensions isolated from the plants grown under the normal Z $n^{2+}$ level (10$^{-6}$ M). The other was that the different concentrations of Z $n^{2+}$ was applied in each growth medium. Then, it was not added into the chloroplasts suspensions isolated from the plants. PS II activity in both way of the treatments was more severely inhibited than PS I by the increment of Z $n^{2+}$ concentration. The photophosphorylation activity measured by pH measurement was gradually decreased with the increase of Z $n^{2+}$ concentration in both ways, too. However, it was shown that M $n^{2+}$ could be near fully overcome the inhibitory effect of Z $n^{2+}$ in PS II, and $Mg^{2+}$ could also reduce the Z $n^{2+}$ inhibition in the photophosphorylation. In the low concentrations of $Mg^{2+}$ (3 to 5$\times$10$^{-3}$ M) in the suspension, Z $n^{2+}$ (2$\times$10$^{-5}$ M) could increase the activity of photophosphorylation. As compares to other cations, Z $n^{2+}$ caused less inhibitory effect on the photophosphorylation activity than Cu, Cd, but more than Pb and Ni. It may be assumed that a complex from reaction of Z $n^{2+}$ and mercaptoethanol was produced and it could reduce the stability of CPI band during SDS-PAGE.ld reduce the stability of CPI band during SDS-PAGE.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.163-176
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• 2014

Social media is becoming the platform for users to communicate their activities, status, emotions, and experiences to other people. In recent years, microblogs, such as Twitter, have gained in popularity because of its ease of use, speed, and reach. Compared to a conventional web blog, a microblog lowers users' efforts and investment for content generation by recommending shorter posts. There has been a lot research into capturing the social phenomena and analyzing the chatter of microblogs. However, measuring television ratings has been given little attention so far. Currently, the most common method to measure TV ratings uses an electronic metering device installed in a small number of sampled households. Microblogs allow users to post short messages, share daily updates, and conveniently keep in touch. In a similar way, microblog users are interacting with each other while watching television or movies, or visiting a new place. In order to measure TV ratings, some features are significant during certain hours of the day, or days of the week, whereas these same features are meaningless during other time periods. Thus, the importance of features can change during the day, and a model capturing the time sensitive relevance is required to estimate TV ratings. Therefore, modeling time-related characteristics of features should be a key when measuring the TV ratings through microblogs. We show that capturing time-dependency of features in measuring TV ratings is vitally necessary for improving their accuracy. To explore the relationship between the content of microblogs and TV ratings, we collected Twitter data using the Get Search component of the Twitter REST API from January 2013 to October 2013. There are about 300 thousand posts in our data set for the experiment. After excluding data such as adverting or promoted tweets, we selected 149 thousand tweets for analysis. The number of tweets reaches its maximum level on the broadcasting day and increases rapidly around the broadcasting time. This result is stems from the characteristics of the public channel, which broadcasts the program at the predetermined time. From our analysis, we find that count-based features such as the number of tweets or retweets have a low correlation with TV ratings. This result implies that a simple tweet rate does not reflect the satisfaction or response to the TV programs. Content-based features extracted from the content of tweets have a relatively high correlation with TV ratings. Further, some emoticons or newly coined words that are not tagged in the morpheme extraction process have a strong relationship with TV ratings. We find that there is a time-dependency in the correlation of features between the before and after broadcasting time. Since the TV program is broadcast at the predetermined time regularly, users post tweets expressing their expectation for the program or disappointment over not being able to watch the program. The highly correlated features before the broadcast are different from the features after broadcasting. This result explains that the relevance of words with TV programs can change according to the time of the tweets. Among the 336 words that fulfill the minimum requirements for candidate features, 145 words have the highest correlation before the broadcasting time, whereas 68 words reach the highest correlation after broadcasting. Interestingly, some words that express the impossibility of watching the program show a high relevance, despite containing a negative meaning. Understanding the time-dependency of features can be helpful in improving the accuracy of TV ratings measurement. This research contributes a basis to estimate the response to or satisfaction with the broadcasted programs using the time dependency of words in Twitter chatter. More research is needed to refine the methodology for predicting or measuring TV ratings.

• Applied Microscopy
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• v.23 no.2
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• pp.27-40
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• 1993

This study was taken to examine the light microscopic and ultrastructural changes of gill and kidney of female tilapia{Oreochromis niloticus) adapted in 0%o, 10%o, 20%o, and 30%o salt concentrations, respectively, by light, scanning and transmission electron microscope. The results obtained in these experiments were summarized as follows: Gill chloride cell hyperplasia, gill lamellar epithelial separation, kidney glomerular shrinkage, blood congestion in kidneys and deposition of hyalin droplets in kidney glomeruli, tubules were the histological alterations in Oreochromis niloticus. Incidence and severity of gill chloride cell hyperplasia rapidly increased together with increase of salinity, and the number of chloride cells in gill lamellae rapidly increased in response to high external NaCl concentrations. The ultrastructure by scanning electron microscope(SEM) indicated that the gill secondary lamella of tilapia(Oreochromis niloticus) exposed to seawater, were characterized by rough convoluted surfaces during the adaptation. Transmission electron microscopy(TEM) indicated that mitochondria in chloride cells exposed to seawater, were both large and elongate and contained well-developed cristae. TEM also showed the increased chloride cells exposed to seawater. The presence of two mitochondria-rich cell types is discussed with regard to their possible role in the hypoosmoregulatory changes which occur during seawater-adaptation. Most Oreochromis niloticus adapted in seawater had an occasional glomerulus completely filling Bowman's capsule in kidney, and glomerular shrinkage was occurred higher in kidney tissues of individuals living in 10%o, 20%o, 30%o of seawater than in those living in 0%o of freshwater, and blood congestion was occurred severer in kidney tissues of individuals living 20%o, 30%o of seawater than in those living in 10%o of seawater. There were decreases in the glomerular area and the nuclear area in the main segments of the nephron, and that the nuclear areas of the nephron cells in seawater-adapted tilapia were of smaller size than those from freshwater-adapted fish. Our findings demonstrated that Oreochromis niloticus tolerated moderately saline environment and the increased body weight living in 30%o was relatively higher than that living in 10%o in spite of histopathological changes.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.29-45
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• 2012

Bond rating is regarded as an important event for measuring financial risk of companies and for determining the investment returns of investors. As a result, it has been a popular research topic for researchers to predict companies' credit ratings by applying statistical and machine learning techniques. The statistical techniques, including multiple regression, multiple discriminant analysis (MDA), logistic models (LOGIT), and probit analysis, have been traditionally used in bond rating. However, one major drawback is that it should be based on strict assumptions. Such strict assumptions include linearity, normality, independence among predictor variables and pre-existing functional forms relating the criterion variablesand the predictor variables. Those strict assumptions of traditional statistics have limited their application to the real world. Machine learning techniques also used in bond rating prediction models include decision trees (DT), neural networks (NN), and Support Vector Machine (SVM). Especially, SVM is recognized as a new and promising classification and regression analysis method. SVM learns a separating hyperplane that can maximize the margin between two categories. SVM is simple enough to be analyzed mathematical, and leads to high performance in practical applications. SVM implements the structuralrisk minimization principle and searches to minimize an upper bound of the generalization error. In addition, the solution of SVM may be a global optimum and thus, overfitting is unlikely to occur with SVM. In addition, SVM does not require too many data sample for training since it builds prediction models by only using some representative sample near the boundaries called support vectors. A number of experimental researches have indicated that SVM has been successfully applied in a variety of pattern recognition fields. However, there are three major drawbacks that can be potential causes for degrading SVM's performance. First, SVM is originally proposed for solving binary-class classification problems. Methods for combining SVMs for multi-class classification such as One-Against-One, One-Against-All have been proposed, but they do not improve the performance in multi-class classification problem as much as SVM for binary-class classification. Second, approximation algorithms (e.g. decomposition methods, sequential minimal optimization algorithm) could be used for effective multi-class computation to reduce computation time, but it could deteriorate classification performance. Third, the difficulty in multi-class prediction problems is in data imbalance problem that can occur when the number of instances in one class greatly outnumbers the number of instances in the other class. Such data sets often cause a default classifier to be built due to skewed boundary and thus the reduction in the classification accuracy of such a classifier. SVM ensemble learning is one of machine learning methods to cope with the above drawbacks. Ensemble learning is a method for improving the performance of classification and prediction algorithms. AdaBoost is one of the widely used ensemble learning techniques. It constructs a composite classifier by sequentially training classifiers while increasing weight on the misclassified observations through iterations. The observations that are incorrectly predicted by previous classifiers are chosen more often than examples that are correctly predicted. Thus Boosting attempts to produce new classifiers that are better able to predict examples for which the current ensemble's performance is poor. In this way, it can reinforce the training of the misclassified observations of the minority class. This paper proposes a multiclass Geometric Mean-based Boosting (MGM-Boost) to resolve multiclass prediction problem. Since MGM-Boost introduces the notion of geometric mean into AdaBoost, it can perform learning process considering the geometric mean-based accuracy and errors of multiclass. This study applies MGM-Boost to the real-world bond rating case for Korean companies to examine the feasibility of MGM-Boost. 10-fold cross validations for threetimes with different random seeds are performed in order to ensure that the comparison among three different classifiers does not happen by chance. For each of 10-fold cross validation, the entire data set is first partitioned into tenequal-sized sets, and then each set is in turn used as the test set while the classifier trains on the other nine sets. That is, cross-validated folds have been tested independently of each algorithm. Through these steps, we have obtained the results for classifiers on each of the 30 experiments. In the comparison of arithmetic mean-based prediction accuracy between individual classifiers, MGM-Boost (52.95%) shows higher prediction accuracy than both AdaBoost (51.69%) and SVM (49.47%). MGM-Boost (28.12%) also shows the higher prediction accuracy than AdaBoost (24.65%) and SVM (15.42%)in terms of geometric mean-based prediction accuracy. T-test is used to examine whether the performance of each classifiers for 30 folds is significantly different. The results indicate that performance of MGM-Boost is significantly different from AdaBoost and SVM classifiers at 1% level. These results mean that MGM-Boost can provide robust and stable solutions to multi-classproblems such as bond rating.

• Journal of Chest Surgery
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• v.39 no.4 s.261
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• pp.289-297
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• 2006

Background: Surgery for Stanford type A aortic dissection shows a high operative mortality rate and frequent postoperative brain injury. This study was designed to find out the risk factors leading to operative mortality and brain injury after surgical repair in patients with type A aortic dissection. Material and Method: One hundred and eleven patients with type A aortic dissection who underwent surgical repair between February, 1995 and January 2005 were reviewed retrospectively. There were 99 acute dissections and 12 chronic dissections. Univariate and multivariate analysis were performed to identify risk factors of operative mortality and brain injury. Resuit: Hospital mortality occurred in 6 patients (5.4%). Permanent neurologic deficit occurred in 8 patients (7.2%) and transient neurologic deficit in 4 (3.6%). Overall 1, 5, 7 year survival rate was 94.4, 86.3, and 81.5%, respectively. Univariate analysis revealed 4 risk factors to be statistically significant as predictors of mortality: previous chronic type III dissection, emergency operation, intimal tear in aortic arch, and deep hypothemic circulatory arrest (DHCA) for more than 45 minutes. Multivariate analysis revealed previous chronic type III aortic dissection (odds ratio (OR) 52.2), and DHCA for more than 45 minutes (OR 12.0) as risk factors of operative mortality. Pathological obesity (OR 12.9) and total arch replacement (OR 8.5) were statistically significant risk factors of brain injury in multivariate analysis. Conclusion: The result of surgical repair for Stanford type A aortic dissection was good when we took into account the mortality rate, the incidence of neurologic injury, and the long-term survival rate. Surgery of type A aortic dissection in patients with a history of chronic type III dissection may increase the risk of operative mortality. Special care should be taken and efforts to reduce the hypothermic circulatory arrest time should alway: be kept in mind. Surgeons who are planning to operate on patients with pathological obesity, or total arch replacement should be seriously consider for there is a higher risk of brain injury.