• Horticultural Science & Technology
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• v.32 no.4
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• pp.499-506
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• 2014

This research was conducted to develop root media containing ground and aged pine bark (GAPB) and ground and raw pine bark (GRPB). After analysis of physico chemical properties, the pine barks were blended with peat moss (PM) or coir dust (CD) in various ratios to formulate 12 root media. Then, two out of 12 root media were chosen based on the physical properties for further experiments. The pre-planting nutrient charge fertilizers (PNCF) were incorporated into two root media and chemical properties were analysed again. The total porosity (TP), container capacity (CC), and air-filled porosity (AFP) of GAPB were 78.7%. 39.4%, and 38.3%, respectively, while those of GRPB were 74.7%, 41.2%, and 33.4%, respectively. The percentage of easily available water (EAW, from CC to 4.90 kPa tension) and buffering water (BW, 4.91-9.81 kPa tension) in GAPB were 12.7% and 8.5%, respectively, which were a little lower than the 13.5% and 8.8% in GRPB. The pH and EC were not different significantly, but cation exchange capacity was different between the two pine barks (GAPB: pH 5.26, EC $0.61dS{\cdot}m^{-1}$, CEC $15.7meq{\cdot}100g^{-1}$; GRPB: pH 5.19, EC $0.32dS{\cdot}m^{-1}$, CEC $9.32meq{\cdot}100g^{-1}$). The concentrations of exchangeable cations in GAPB were Ca 0.32, K 0.05, Mg 0.27 and $0.12cmol+{\cdot}kg^{-1}$, whereas those in GRPB were Ca 0.28, K 0.08, Mg 0.25 and $0.09cmol+{\cdot}kg^{-1}$. The concentrations of $PO_4$-P, $NH_4$-N and $NO_3$-N were 485.8, 0.62 and $0.91mg{\cdot}L^{-1}$ in GAPB and 578, 1.00 and $0.82mg{\cdot}L^{-1}$ in GRPB, respectively, when those were analyzed in the solution of the saturated paste. The TP, CC and AFP in the two selected media were 89.3 and 76.3, and 13.0% in PM+GAPB (8:2, v/v) and 88.2, 68.2 and 20.0% in CD+GRPB (8:2), respectively. The pHs and ECs were 3.8 and $0.24dS{\cdot}m^{-1}$ in PM+GAPB which were a little lower than 5.8 and $0.65dS{\cdot}m^{-1}$ in CD+GRPB. However, the pHs analysed before and after incorporation of PNCF in the two root media did not show large differences. This is because the solubility of dolomitic lime is very low, and the pH it is expected to rise gradually when crops are cultivated int he root media. The information obtained in this study should facilitate effective formulation of root media containing pine bark.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.3
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• pp.425-430
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• 2014

Foods contain various nutrients such as carbohydrates, protein, oil, vitamins, and minerals. Among them, carbohydrates, protein, and oil are the main constituents of foods. Usually, these constituents are analyzed by the Kjeldahl and Soxhlet method and so on. However, these analytical methods are complex, costly, and time-consuming. Thus, this study aimed to rapidly and effectively analyze carbohydrate, protein, and oil contents with near-infrared reflectance spectroscopy (NIRS). A total of 517 food samples were measured within the wavelength range of 400 to 2,500 nm. Exactly 412 food calibration samples and 162 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of carbohydrates, the most accurate equation was obtained under 1, 4, 5, 1 (1st derivative, 4 nm gap, 5 points smoothing, and 1 point second smoothing) math treatment conditions using the weighted MSC (multiplicative scatter correction) scatter correction method with MPLS (modified partial least square) regression. In the case of protein and oil, the best equation were obtained under 2, 5, 5, 3 and 1, 1, 1, 1 conditions, respectively, using standard MSC and standard normal variate only scatter correction methods with MPLS regression. Calibrations of these NIRS equations showed a very high coefficient of determination in calibration ($R^2$: carbohydrates, 0.971; protein, 0.974; oil, 0.937) and low standard error of calibration (carbohydrates, 4.066; protein, 1.080; oil, 1.890). Optimal equation conditions were applied to a validation set of 162 samples. Validation results of these NIRS equations showed a very high coefficient of determination in prediction ($r^2$: carbohydrates, 0.987; protein, 0.970; oil, 0.947) and low standard error of prediction (carbohydrates, 2.515; protein, 1.144; oil, 1.370). Therefore, these NIRS equations can be applicable for determination of carbohydrates, proteins, and oil contents in various foods.

• Journal of Global Scholars of Marketing Science
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• v.18 no.2
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• pp.89-111
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• 2008

Due to the development of digital technology, studies regarding smart wear integrating daily life have rapidly increased. However, consumer research about perception and attitude toward smart clothing hardly could find. The purpose of this study was to identify innovative characteristics and perceived risk of smart clothing and to analyze the influences of theses factors on product attitudes and intention to adopt. Specifically, five hypotheses were established. H1: Perceived attributes of smart clothing except for complexity would have positive relations to product attitude or purchase intention, while complexity would be opposite. H2: Product attitude would have positive relation to purchase intention. H3: Product attitude would have a mediating effect between perceived attributes and purchase intention. H4: Perceived risks of smart clothing would have negative relations to perceived attributes except for complexity, and positive relations to complexity. H5: Product attitude would have a mediating effect between perceived risks and purchase intention. A self-administered questionnaire was developed based on previous studies. After pretest, the data were collected during September, 2006, from university students in Korea who were relatively sensitive to innovative products. A total of 300 final useful questionnaire were analyzed by SPSS 13.0 program. About 60.3% were male with the mean age of 21.3 years old. About 59.3% reported that they were aware of smart clothing, but only 9 respondents purchased it. The mean of attitudes toward smart clothing and purchase intention was 2.96 (SD=.56) and 2.63 (SD=.65) respectively. Factor analysis using principal components with varimax rotation was conducted to identify perceived attribute and perceived risk dimensions. Perceived attributes of smart wear were categorized into relative advantage (including compatibility), observability (including triability), and complexity. Perceived risks were identified into physical/performance risk, social psychological risk, time loss risk, and economic risk. Regression analysis was conducted to test five hypotheses. Relative advantage and observability were significant predictors of product attitude (adj $R^2$=.223) and purchase intention (adj $R^2$=.221). Complexity showed negative influence on product attitude. Product attitude presented significant relation to purchase intention (adj $R^2$=.692) and partial mediating effect between perceived attributes and purchase intention (adj $R^2$=.698). Therefore hypothesis one to three were accepted. In order to test hypothesis four, four dimensions of perceived risk and demographic variables (age, gender, monthly household income, awareness of smart clothing, and purchase experience) were entered as independent variables in the regression models. Social psychological risk, economic risk, and gender (female) were significant to predict relative advantage (adj $R^2$=.276). When perceived observability was a dependent variable, social psychological risk, time loss risk, physical/performance risk, and age (younger) were significant in order (adj $R^2$=.144). However, physical/performance risk was positively related to observability. The more Koreans seemed to be observable of smart clothing, the more increased the probability of physical harm or performance problems received. Complexity was predicted by product awareness, social psychological risk, economic risk, and purchase experience in order (adj $R^2$=.114). Product awareness was negatively related to complexity, meaning high level of product awareness would reduce complexity of smart clothing. However, purchase experience presented positive relation with complexity. It appears that consumers can perceive high level of complexity when they are actually consuming smart clothing in real life. Risk variables were positively related with complexity. That is, in order to decrease complexity, it is also necessary to consider minimizing anxiety factors about social psychological wound or loss of money. Thus, hypothesis 4 was partially accepted. Finally, in testing hypothesis 5, social psychological risk and economic risk were significant predictors for product attitude (adj $R^2$=.122) and purchase intention (adj $R^2$=.099) respectively. When attitude variable was included with risk variables as independent variables in the regression model to predict purchase intention, only attitude variable was significant (adj $R^2$=.691). Thus attitude variable presented full mediating effect between perceived risks and purchase intention, and hypothesis 5 was accepted. Findings would provide guidelines for fashion and electronic businesses who aim to create and strengthen positive attitude toward smart clothing. Marketers need to consider not only functional feature of smart clothing, but also practical and aesthetic attributes, since appropriateness for social norm or self image would reduce uncertainty of psychological or social risk, which increase relative advantage of smart clothing. Actually social psychological risk was significantly associated to relative advantage. Economic risk is negatively associated with product attitudes as well as purchase intention, suggesting that smart-wear developers have to reflect on price ranges of potential adopters. It will be effective to utilize the findings associated with complexity when marketers in US plan communication strategy.

• Asia pacific journal of information systems
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• v.21 no.4
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• pp.63-104
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• 2011

The study examines the relationships among employee's goal orientation, IT personnel competency, personal effectiveness. The goal orientation includes learning goal orientation, performance approach goal orientation, and performance avoid goal orientation. Personal effectiveness consists of personal work satisfaction and personal work performance. In general, IT personnel competency refers to IT expert's skills, expertise, and knowledge required to perform IT activities in organizations. However, due to the advent of the internet and the generalization of IT, IT personnel competency turns out to be an important competency of technological experts as well as employees in organizations. While the competency of IT itself is important, the appropriate harmony between IT personnel's business capability and technological capability enhances the value of human resources and thus provides organizations with sustainable competitive advantages. The rapid pace of organization change places increased pressure on employees to continually update their skills and adapt their behavior to new organizational realities. This challenge raises a number of important questions concerning organizational behavior? Why do some employees display remarkable flexibility in their behavioral responses to changes in the organization, whereas others firmly resist change or experience great stress when faced with the need to alter behavior? Why do some employees continually strive to improve themselves over their life span, whereas others are content to forge through life using the same basic knowledge and skills? Why do some employees throw themselves enthusiastically into challenging tasks, whereas others avoid challenging tasks? The goal orientation proposed by organizational psychology provides at least a partial answer to these questions. Goal orientations refer to stable personally characteristics fostered by "self-theories" about the nature and development of attributes (such as intelligence, personality, abilities, and skills) people have. Self-theories are one's beliefs and goal orientations are achievement motivation revealed in seeking goals in accordance with one's beliefs. The goal orientations include learning goal orientation, performance approach goal orientation, and performance avoid goal orientation. Specifically, a learning goal orientation refers to a preference to develop the self by acquiring new skills, mastering new situations, and improving one's competence. A performance approach goal orientation refers to a preference to demonstrate and validate the adequacy of one's competence by seeking favorable judgments and avoiding negative judgments. A performance avoid goal orientation refers to a preference to avoid the disproving of one's competence and to avoid negative judgements about it, while focusing on performance. And the study also examines the moderating role of work career of employees to investigate the difference in the relationship between IT personnel competency and personal effectiveness. The study analyzes the collected data using PASW 18.0 and and PLS(Partial Least Square). The study also uses PLS bootstrapping algorithm (sample size: 500) to test research hypotheses. The result shows that the influences of both a learning goal orientation (${\beta}$ = 0.301, t = 3.822, P < 0.000) and a performance approach goal orientation (${\beta}$ = 0.224, t = 2.710, P < 0.01) on IT personnel competency are positively significant, while the influence of a performance avoid goal orientation(${\beta}$ = -0.142, t = 2.398, p < 0.05) on IT personnel competency is negatively significant. The result indicates that employees differ in their psychological and behavioral responses according to the goal orientation of employees. The result also shows that the impact of a IT personnel competency on both personal work satisfaction(${\beta}$ = 0.395, t = 4.897, P < 0.000) and personal work performance(${\beta}$ = 0.575, t = 12.800, P < 0.000) is positively significant. And the impact of personal work satisfaction(${\beta}$ = 0.148, t = 2.432, p < 0.05) on personal work performance is positively significant. Finally, the impacts of control variables (gender, age, type of industry, position, work career) on the relationships between IT personnel competency and personal effectiveness(personal work satisfaction work performance) are partly significant. In addition, the study uses PLS algorithm to find out a GoF(global criterion of goodness of fit) of the exploratory research model which includes a mediating variable, IT personnel competency. The result of analysis shows that the value of GoF is 0.45 above GoFlarge(0.36). Therefore, the research model turns out be good. In addition, the study performs a Sobel Test to find out the statistical significance of the mediating variable, IT personnel competency, which is already turned out to have the mediating effect in the research model using PLS. The result of a Sobel Test shows that the values of Z are all significant statistically (above 1.96 and below -1.96) and indicates that IT personnel competency plays a mediating role in the research model. At the present day, most employees are universally afraid of organizational changes and resistant to them in organizations in which the acceptance and learning of a new information technology or information system is particularly required. The problem is due' to increasing a feeling of uneasiness and uncertainty in improving past practices in accordance with new organizational changes. It is not always possible for employees with positive attitudes to perform their works suitable to organizational goals. Therefore, organizations need to identify what kinds of goal-oriented minds employees have, motivate them to do self-directed learning, and provide them with organizational environment to enhance positive aspects in their works. Thus, the study provides researchers and practitioners with a matter of primary interest in goal orientation and IT personnel competency, of which they have been unaware until very recently. Some academic and practical implications and limitations arisen in the course of the research, and suggestions for future research directions are also discussed.

• Journal of the Korean Society for Library and Information Science
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• v.25
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• pp.343-388
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• 1993

The ability of any organization to innovate itself in a rapid change of environment means the existence of the organization. Innovative activity is achieved in different ways according to the objectives of organization. the characteristics of external environmental factors. and various attributes in organization. In the present study. all the existing approaches to the innovative nature of organization were synthetically compared to each other and evaluated: then. for a more rational approach. a research model was built and suggested by establishing the inclusive variables of the innovative nature of library organization and categorizing the types of such nature. Additionally. an empirical. analytical study on such a model was done. That is. paying regard to the fact that innovation has basically a close relation with the circumstantial factors of organization. synthetic, circumstantial relations were clarified. considering the external environmental factors and internal characteristics of organization. In the study. the innovation of library organization was seen in two parts i.e .. the feasible degree of technical innovation and the feasible degree of administrative innovation. Regarding the types of innovative implementation. according to the feasible degree of innovation, four types such as a stationary type. technic-oriented type, organization-oriented type. and technical-socio systematic type were classified. There were nine independent variables-i.e., the scale of organization. available resources of the organization, formalization, differentiation, specialization. decentralization, recognizant degree of the technical attribute. degree of response to the change of technical environment, and professional activities. There were three subordinate variables - i.e., technical innovation, administrative innovation. and the performance of organization. Through establishment of such variables, the factors which might influence the innovation of library organization were understood, and with the types of the innovative implementation of library organization being classified according to the feasible degree of innovation. the characteristics of library organization were reviewed in the light of each type. Also. the performance of library organization according to the types of the innovative implementation of library organization was analyzed. and the relations between the types of innovative implementation according to circumstantial variables and the performance of library organization were clarified. In order to clarify the adequacy of the research model in the methodology of empirical study, data were collected from 72 university libraries and 38 special libraries. and for a hypothetical test of the research model. an analysis of correlations, a stepwise regression analysis. and One Way ANOVA were utilized. The following are the major results or findings from the study 1) It appeared there is a trend that the bigger the scale of organization and available resources are, the more active the professional activity of the managerial class is, and the higher the recognizant degree of technical environment (recognizant degree of technical attributes and the degree of response t9 the change of technical environment) is, the higher the feasible degree of innovation becomes. 2) It appeared that among the variables influencing the feasible degree of technical innovation, the order from the variable influencing most was first, the recognizant degree of technical innovation: second, the available resources of organization: and third, professional activity. Regarding the variables influencing the feasible degree of administrative innovation from the most influential variable, it appeared they were the available resources of organization, the differentiation of organization. and the degree of response to the change of technical environment. 3) It appeared that the higher the educational level of the managerial class is, the more active the professional activity becomes. It seemed there is a trend that the group of library managers whose experience as a librarian was at the middle level(three years to six years of experience) was more active in research activity than the group of library managers whose experience as a librarian was at a higher level(more than ten years). Also, it appeared there is a trend that the lower the age of library managers is, the higher the recognizant degree of technical attributes becomes. and the group of library managers whose experience as a librarian was at the middle level (three years to six years of experience) recognized more affirmatively the technical aspect than the group of library managers whose experience as a librarian was at a higher level(more than 10 years). Also, it appeared that, when the activity of the professional association and research activity are active, the recognizant degree of technology becomes higher, and as a result. it influences the innovative nature of organization(the feasible degree of technical innovation and the feasible degree of administrative innovation). 4) As a result of the comparison and analysis of the characteristics of library organization according to the types of innovative implementation of library organization. it was indicated there is a trend that the larger the available resources of library organization, the higher the organic nature of organization such as differentiation. decentralization, etc., and the higher the level of the operation of system development, the more the type of the innovative implementation of library organization becomes the technical-socio systematic type which is higher both in the practical degrees of technical innovation and administrative innovation. 5) As a result of the comparison and analysis of the relations between the types of innovative implementation and the performance of organization, it appeared that the order from the highest performance of organization is the technical-socio systematic type, then the technic-oriented type, the organization­oriented type, and finally the stationary type which is lowest in such performance. That is, it demonstrated that, since the performance of library organization is highest in the library of the technical-socio systematic type while it is lowest in the library whose practical degrees in both technical innovation and administrative innovation are low, the performance of library organization differs significantly according to the types of innovative implementation of library organization. The present study has extracted the factors influencing innovation, classified systematically the types of innovative implementation, and inferred the synthetical, circumstantial correlations between the types and the performance of organization, and empirically inspected those factors. However, due to the present study's restrictive matters and the limit of the research design, results from the study should be more prudently interpreted. Also, the present study, as an investigative study of the types of innovative implementation, with few preceding studies, requires more complete hypothetical inference based on the results of the present study. In other words, if more systematical studies are given to understanding the relations, it will devote the suggestion and demonstration of a more useful theory.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.171-183
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• 2012

In the development of technologies, the exhibition industry has received much attention from governments and companies as an important way of marketing activities. Also, the exhibitors have considered the exhibition as new channels of marketing activities. However, the growing size of exhibitions for net square feet and the number of visitors naturally creates the competitive environment for them. Therefore, to make use of the effective marketing tools in these environments, they have planned and implemented many promotion technics. Especially, through smart environment which makes them provide real-time information for visitors, they can implement various kinds of promotion. However, promotions ignoring visitors' various needs and preferences can lose the original purposes and functions of them. That is, as indiscriminate promotions make visitors feel like spam, they can't achieve their purposes. Therefore, they need an approach using STP strategy which segments visitors through right evidences (Segmentation), selects the target visitors (Targeting), and give proper services to them (Positioning). For using STP Strategy in the smart exhibition environment, we consider these characteristics of it. First, an exhibition is defined as market events of a specific duration, which are held at intervals. According to this, exhibitors who plan some promotions should different events and promotions in each exhibition. Therefore, when they adopt traditional STP strategies, a system can provide services using insufficient information and of existing visitors, and should guarantee the performance of it. Second, to segment automatically, cluster analysis which is generally used as data mining technology can be adopted. In the smart exhibition environment, information of visitors can be acquired in real-time. At the same time, services using this information should be also provided in real-time. However, many clustering algorithms have scalability problem which they hardly work on a large database and require for domain knowledge to determine input parameters. Therefore, through selecting a suitable methodology and fitting, it should provide real-time services. Finally, it is needed to make use of data in the smart exhibition environment. As there are useful data such as booth visit records and participation records for events, the STP strategy for the smart exhibition is based on not only demographical segmentation but also behavioral segmentation. Therefore, in this study, we analyze a case of the promotion methodology which exhibitors can provide a differentiated service to segmented visitors in the smart exhibition environment. First, considering characteristics of the smart exhibition environment, we draw evidences of segmentation and fit the clustering methodology for providing real-time services. There are many studies for classify visitors, but we adopt a segmentation methodology based on visitors' behavioral traits. Through the direct observation, Veron and Levasseur classify visitors into four groups to liken visitors' traits to animals (Butterfly, fish, grasshopper, and ant). Especially, because variables of their classification like the number of visits and the average time of a visit can estimate in the smart exhibition environment, it can provide theoretical and practical background for our system. Next, we construct a pilot system which automatically selects suitable visitors along the objectives of promotions and instantly provide promotion messages to them. That is, based on the segmentation of our methodology, our system automatically selects suitable visitors along the characteristics of promotions. We adopt this system to real exhibition environment, and analyze data from results of adaptation. As a result, as we classify visitors into four types through their behavioral pattern in the exhibition, we provide some insights for researchers who build the smart exhibition environment and can gain promotion strategies fitting each cluster. First, visitors of ANT type show high response rate for promotion messages except experience promotion. So they are fascinated by actual profits in exhibition area, and dislike promotions requiring a long time. Contrastively, visitors of GRASSHOPPER type show high response rate only for experience promotion. Second, visitors of FISH type appear favors to coupon and contents promotions. That is, although they don't look in detail, they prefer to obtain further information such as brochure. Especially, exhibitors that want to give much information for limited time should give attention to visitors of this type. Consequently, these promotion strategies are expected to give exhibitors some insights when they plan and organize their activities, and grow the performance of them.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.39-53
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• 2013

The emergence of the internet technology and SNS has increased the information flow and has changed the way people to communicate from one-way to two-way communication. Users not only consume and share the information, they also can create and share it among their friends across the social network service. It also changes the Social Media behavior to become one of the most important communication tools which also includes Social TV. Social TV is a form which people can watch a TV program and at the same share any information or its content with friends through Social media. Social News is getting popular and also known as a Participatory Social Media. It creates influences on user interest through Internet to represent society issues and creates news credibility based on user's reputation. However, the conventional platforms in news services only focus on the news recommendation domain. Recent development in SNS has changed this landscape to allow user to share and disseminate the news. Conventional platform does not provide any special way for news to be share. Currently, Social News Service only allows user to access the entire news. Nonetheless, they cannot access partial of the contents which related to users interest. For example user only have interested to a partial of the news and share the content, it is still hard for them to do so. In worst cases users might understand the news in different context. To solve this, Social News Service must provide a method to provide additional information. For example, Yovisto known as an academic video searching service provided time dependent metadata from the video. User can search and watch partial of video content according to time dependent metadata. They also can share content with a friend in social media. Yovisto applies a method to divide or synchronize a video based whenever the slides presentation is changed to another page. However, we are not able to employs this method on news video since the news video is not incorporating with any power point slides presentation. Segmentation method is required to separate the news video and to creating time dependent metadata. In this work, In this paper, a time dependent metadata-based framework is proposed to segment news contents and to provide time dependent metadata so that user can use context information to communicate with their friends. The transcript of the news is divided by using the proposed story segmentation method. We provide a tag to represent the entire content of the news. And provide the sub tag to indicate the segmented news which includes the starting time of the news. The time dependent metadata helps user to track the news information. It also allows them to leave a comment on each segment of the news. User also may share the news based on time metadata as segmented news or as a whole. Therefore, it helps the user to understand the shared news. To demonstrate the performance, we evaluate the story segmentation accuracy and also the tag generation. For this purpose, we measured accuracy of the story segmentation through semantic similarity and compared to the benchmark algorithm. Experimental results show that the proposed method outperforms benchmark algorithms in terms of the accuracy of story segmentation. It is important to note that sub tag accuracy is the most important as a part of the proposed framework to share the specific news context with others. To extract a more accurate sub tags, we have created stop word list that is not related to the content of the news such as name of the anchor or reporter. And we applied to framework. We have analyzed the accuracy of tags and sub tags which represent the context of news. From the analysis, it seems that proposed framework is helpful to users for sharing their opinions with context information in Social media and Social news.

• The Korean Journal of Malacology
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• v.22 no.2
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• pp.125-134
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• 2006

We investigated the reproductive cycle of the hard clam, Meretrix petechialis with its gonadal development by histological observations. The seasonal changes in biochemical component of the adductor muscle, visceral mass, foot muscle and mantle of the clam were studied by biochemical analysis, from January to December, 2002. The reproductive cycle of this species can be divided into five successive stages: early stage (January to March), late active stage (February to May), ripe stage (April to August), partially spawned stage (July to August) and spent/inactive stage (September to January). Total protein content in the visceral mass was over two times higher than that in the adductor muscle. Monthly changes of total protein content in the adductor muscle were not statistically significant (ANOVA, p = 0.071), while the changes in the visceral mass were significant (p < 0.001). Total protein content in visceral mass was higher during the early active, late active, and ripe stages (from January to May), while the lowest in July. Glycogen content in the adductor muscle was higher than that in the visceral mass. Monthly changes in glycogen contents were statistically significant in both adductor muscle (F = 237.2, p < 0.001) and the visceral mass (F = 64.04, p < 0.001). Glycogen content in the adductor muscle was the highest in the ripe stage (April). Its content was lower in the partially spawned and the spent/inactive stages (June-September). Glycogen contents in the visceral mass were relatively lower until the early active stage, while the highest in the late active stage. RNA content was higher in visceral mass than that in the adductor muscle. Monthly changes in RNA contents were significant in both adductor muscle (F = 195.2, p < 0.001) and visceral mass (F = 78.85, p < 0.001). RNA content in the adductor muscle was high in the early active stage (January-February), and then it decreased rapidly in the late active stage (March-April), thereafter, slightly increased during the partially spawned stage (June-July). RNA content in the visceral mass reached a maximum during the ripe stage (May), and then it decreased rapidly during the partially-spawned stage (June-July). There was significant positive correlation in total protein contents between adductor muscle and visceral mass (r = 0.715, p = 0.020). However, there was no correlation between adductor muscle and visceral mass in glycogen (p = 0.550), while a negative correlation was found between the adductor muscle and visceral mass in RNA (p = 0.518) contents. Especially, changes in RNA content showed a negative correlation between the adductor muscle tissue and visceral mass. Therefore, these results suggest that the nutrient content of the adductor muscle, visceral muscle and foot muscle changed in response to gonadal energy needs.

• The Korean Journal of Nuclear Medicine
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• v.38 no.6
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• pp.486-491
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• 2004

Purpose: factor analysis and independent component analysis (ICA) has been used for handling dynamic image sequences. Theoretical advantages of a newly suggested ICA method, ensemble ICA, leaded us to consider applying this method to the analysis of dynamic myocardial $H_2^{15}O$ PET data. In this study, we quantified patients' blood flow using the ensemble ICA method. Materials and Methods: Twenty subjects underwent $H_2^{15}O$ PET scans using ECAT EXACT 47 scanner and myocardial perfusion SPECT using Vertex scanner. After transmission scanning, dynamic emission scans were initiated simultaneously with the injection of $555{\sim}740$ MBq $H_2^{15}O$. Hidden independent components can be extracted from the observed mixed data (PET image) by means of ICA algorithms. Ensemble learning is a variational Bayesian method that provides an analytical approximation to the parameter posterior using a tractable distribution. Variational approximation forms a lower bound on the ensemble likelihood and the maximization of the lower bound is achieved through minimizing the Kullback-Leibler divergence between the true posterior and the variational posterior. In this study, posterior pdf was approximated by a rectified Gaussian distribution to incorporate non-negativity constraint, which is suitable to dynamic images in nuclear medicine. Blood flow was measured in 9 regions - apex, four areas in mid wall, and four areas in base wall. Myocardial perfusion SPECT score and angiography results were compared with the regional blood flow. Results: Major cardiac components were separated successfully by the ensemble ICA method and blood flow could be estimated in 15 among 20 patients. Mean myocardial blood flow was $1.2{\pm}0.40$ ml/min/g in rest, $1.85{\pm}1.12$ ml/min/g in stress state. Blood flow values obtained by an operator in two different occasion were highly correlated (r=0.99). In myocardium component image, the image contrast between left ventricle and myocardium was 1:2.7 in average. Perfusion reserve was significantly different between the regions with and without stenosis detected by the coronary angiography (P<0.01). In 66 segment with stenosis confirmed by angiography, the segments with reversible perfusion decrease in perfusion SPECT showed lower perfusion reserve values in $H_2^{15}O$ PET. Conclusions: Myocardial blood flow could be estimated using an ICA method with ensemble learning. We suggest that the ensemble ICA incorporating non-negative constraint is a feasible method to handle dynamic image sequence obtained by the nuclear medicine techniques.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.