• Journal of the Korean earth science society
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• v.27 no.4
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• pp.450-463
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• 2006

Seasonal threshold values for fog detection over the ten airport areas within the Korean Peninsula have been derived from the data of polar-orbit Aqua/Terra MODIS and geostationary GOES-9 during a two years. The values are obtained from reflectance at $0.65{\mu}m\;(R_{0.65})$ and the difference in brightness temperature between $3.7{\mu}m\;and\;11{\mu}m\;(T_{3.7-11})$. In order to examine the discrepancy between the threshold values of two kinds of satellites, the following four parameters have been analyzed under the condition of daytime/nighttime and fog/clear-sky, utilizing their simultaneous observations over the Seoul metropolitan area: brightness temperature at $3.7{\mu}m$, the temperature at $11{\mu}m,\;the\;T_{3.7-11}$ for day and night, and the $R_{0.65}$ for daytime. The parameters show significant correlations (r<0.5) in spatial distribution between the two kinds of satellites. The discrepancy between their infrared thresholds is mainly due to the disagreement in their spatial resolutions and spectral bands, particularly at $3.7{\mu}m$. Fog detection from GOES-9 over the nine airport areas except the Cheongju airport has revealed accuracy of 60% in the daytime and 70% in the nighttime, based on statistical verification. The accuracy decreases in foggy cases with twilight, precipitation, short persistence, or the higher cloud above fog. The sensitivity of radiance and reflectance with wavelength has been analyzed in numerical experiments with respect to various meteorological conditions to investigate optical characteristics of the three channels.

• Journal of the Korean Wood Science and Technology
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• v.27 no.2
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• pp.38-45
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• 1999

This study was to figure out proper Formaldehyde/Urea molar ratio of UF resin with satisfactory bonding strength of plywood and properties of particleboard. The six kinds of UF resins were manufactured with F/U molar ratio 1.0, 1.2, 1.4, 1.6, 1.8, and 2.0. The boards were made of three kinds of raw materials : Veneer, Sliver-Particle and Strand-Particle. Manufacturing condition of plywood : amount of mixing resin was 150g/$m^2$. The fourty secs/mm simple-pressing schedule in the pressure 10kgf/$m^2$ was applied for 480mm${\times}$700mm board at the temperature of $110^{\circ}C$ in a hot press. Manufacturing condition of particleboard : Target density was 0.65g/$cm^2$. The stepwise 9 minutes- multi-pressing schedule in the maximum pressure 40kgf/$cm^2$, the minimum pressure 15kgf/$cm^2$ was applied for $480mm{\times}634mm{\times}12mm$ board at the temperature of $150^{\circ}C$ in a hot press. The results are as follows : I. In bonding strength, plywood which was made by F/U molar ratio 1.2 showed the highest value. Other molar ratio resin also gave the satisfied value of KS standard, 7.5kgf/$cm^2$. 2. In internal bond strength of particleboard, Sliver-Particleboard(SLPB) and Strand-Particleboard(STPB) varied respectively from 5.9kgf/$cm^2$ to 4.8kgf/$cm^2$, from 6.7kgf/$cm^2$ to 5.4kgf/$cm^2$. SLPB with F/U=1.2 and STPB with F/U=1.6 had higher IB value. Also, both SLPB and STPB showed lower IB value in F/U molar ratio 2.0 and 1.0. 3. SLPB and STPB with six kinds of UF resin respectively satisfied bending strength of KS standard 150 Type(130kgf/$cm^2$) and 200 Type(180kgf/$cm^2$). Bending strength data for both of SLPB and STPB showed little or no loss from F/U=1.8 to F/U=1.2. Also, STPB was approximately two times higher than that of SLPB. Therefore, the raw material's shape had more effect on bending strength than the FlU molar ratio. 4. F/U=1.6 and 1.4 showed the lower thickness swelling in SLPB and STPB. All of STPBs satisfied thickness swelling of KS standard, under 12%.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.5
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• pp.2711-2717
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• 2016

Background: Through search the possible randomized control trials, we make a renewed meta-analysis in order to assess the impact of aspirin in preventing the recurrence of colorectal adenoma. Materials and Methods: The Medicine/PubMed, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Chinese biomedical literature service system (SinoMed) databases were searched for the related randomized controlled trials until to the April 2016. Three different authors respectively evaluated the quality of studies and extracted data, and we used the STATA software to analyze, investigate heterogeneity between the data, using the fixed-effects model to calculate and merge data. Results: 7 papers were included the renewed meta-analysis, among these studies, two pairs were identified as representing the same study population, with the only difference being the duration of follow-up. Thus there were only five papers included our meta-analysis, and one Chinese paper were also included the work. Results were categorized by the length of follow-up, different kinds of people, varied dose of oral aspirin. The relative of adenoma in patients taking aspirin vs placebo were 0.73 (95% CI 0.55-0.98, P=0.039) with 1 year follow up; 0.84 (95% CI 0.72-0.98, P=0.484) with greater than 1 year follow up; for the advanced adenoma, the RR 0.68 (95% CI 0.49-0.94, P=0.582),for one year; RR=0.75 (95% CI 0.52-1.07, P=0.552) for greater one year. Furthermore the white population could divided into two subgroups according to the different length of follow-up time. When the length of follow-up time less than 3-year, The RR of two subgroups respective were RR=0.86 (95% CI 0.76-0.98, P=0.332), $I^2=0%$, RR=0.68 (95% CI 0.47-0.98, P=0.552), $I^2=64.6%$, But with the extension of follow-up time greater than 2-year, with the white, oral aspirin without considering dose had no efficacy on preventing the recurrence of any adenoma, the RR was 0.86 (95% CI 0.71-1.05, P=0.302), $I^2=16.4%$. Conclusions: This meta-analysis indicated that oral aspirin is associated with a remarkable decrease in the recurrence of any adenoma and advanced adenomas in patients follow-up for 1 year without concerning the dose of aspirin, but with the extension of follow-up time for greater than 1 year, oral aspirin can be effective on preventing the recurrence of any adenoma, but for the advanced adenoma, the result indicated that oral aspirin had no efficacy, According to the inclusion of ethnic groups, we also divided relevant papers into two subgroups as the yellow and white group. Then the follow-up time was less than 3 years, oral aspirin without considering the dose, had an significant efficacy on preventing the recurrence of any adenoma. But with the follow-up greater than 2 years, oral aspirin had no effect in the white.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.43-61
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• 2019

Development of technologies in artificial intelligence has been rapidly increasing with the Fourth Industrial Revolution, and researches related to AI have been actively conducted in a variety of fields such as autonomous vehicles, natural language processing, and robotics. These researches have been focused on solving cognitive problems such as learning and problem solving related to human intelligence from the 1950s. The field of artificial intelligence has achieved more technological advance than ever, due to recent interest in technology and research on various algorithms. The knowledge-based system is a sub-domain of artificial intelligence, and it aims to enable artificial intelligence agents to make decisions by using machine-readable and processible knowledge constructed from complex and informal human knowledge and rules in various fields. A knowledge base is used to optimize information collection, organization, and retrieval, and recently it is used with statistical artificial intelligence such as machine learning. Recently, the purpose of the knowledge base is to express, publish, and share knowledge on the web by describing and connecting web resources such as pages and data. These knowledge bases are used for intelligent processing in various fields of artificial intelligence such as question answering system of the smart speaker. However, building a useful knowledge base is a time-consuming task and still requires a lot of effort of the experts. In recent years, many kinds of research and technologies of knowledge based artificial intelligence use DBpedia that is one of the biggest knowledge base aiming to extract structured content from the various information of Wikipedia. DBpedia contains various information extracted from Wikipedia such as a title, categories, and links, but the most useful knowledge is from infobox of Wikipedia that presents a summary of some unifying aspect created by users. These knowledge are created by the mapping rule between infobox structures and DBpedia ontology schema defined in DBpedia Extraction Framework. In this way, DBpedia can expect high reliability in terms of accuracy of knowledge by using the method of generating knowledge from semi-structured infobox data created by users. However, since only about 50% of all wiki pages contain infobox in Korean Wikipedia, DBpedia has limitations in term of knowledge scalability. This paper proposes a method to extract knowledge from text documents according to the ontology schema using machine learning. In order to demonstrate the appropriateness of this method, we explain a knowledge extraction model according to the DBpedia ontology schema by learning Wikipedia infoboxes. Our knowledge extraction model consists of three steps, document classification as ontology classes, proper sentence classification to extract triples, and value selection and transformation into RDF triple structure. The structure of Wikipedia infobox are defined as infobox templates that provide standardized information across related articles, and DBpedia ontology schema can be mapped these infobox templates. Based on these mapping relations, we classify the input document according to infobox categories which means ontology classes. After determining the classification of the input document, we classify the appropriate sentence according to attributes belonging to the classification. Finally, we extract knowledge from sentences that are classified as appropriate, and we convert knowledge into a form of triples. In order to train models, we generated training data set from Wikipedia dump using a method to add BIO tags to sentences, so we trained about 200 classes and about 2,500 relations for extracting knowledge. Furthermore, we evaluated comparative experiments of CRF and Bi-LSTM-CRF for the knowledge extraction process. Through this proposed process, it is possible to utilize structured knowledge by extracting knowledge according to the ontology schema from text documents. In addition, this methodology can significantly reduce the effort of the experts to construct instances according to the ontology schema.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.296-310
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• 1995

Classification of spatial units into meaningful sets is an important procedure in spatial analysis. It is crucial in characterizing and identifying spatial structures. But traditional classification methods such as cluster analysis require an exact database and impose a clear-cut boundary between classes. Scrutiny of realistic classification problems, however, reveals that available infermation may be vague and that the boundary may be ambiguous. The weakness of conventional methods is that they fail to capture the fuzzy data and the transition between classes. Fuzzy subsets theory is useful for solving these problems. This paper aims to come to the understanding of theoretical foundations of fuzzy spatial analysis, and to find the characteristics of fuzzy classification methods. It attempts to do so through the literature review and the case study of urban classification of the Cities and Eups of Kyung-Nam Province. The main findings are summarized as follows: 1. Following Dubois and Prade, fuzzy information has an imprecise and/or uncertain evaluation. In geography, fuzzy informations about spatial organization, geographical space perception and human behavior are frequent. But the researcher limits his work to numerical data processing and he does not consider spatial fringe. Fuzzy spatial analysis makes it possible to include the interface of groups in classification. 2. Fuzzy numerical taxonomic method is settled by Deloche, Tranquis, Ponsard and Leung. Depending on the data and the method employed, groups derived may be mutually exclusive or they may overlap to a certain degree. Classification pattern can be derived for each degree of similarity/distance $\alpha$. By takina the values of $\alpha$ in ascending or descending order, the hierarchical classification is obtained. 3. Kyung-Nam Cities and Eups were classified by fuzzy discrete classification, fuzzy conjoint classification and cluster analysis according to the ratio of number of persons employed in industries. As a result, they were divided into several groups which had homogeneous characteristies. Fuzzy discrete classification and cluste-analysis give clear-cut boundary, but fuzzy conjoint classification delimit the edges and cores of urban classification. 4. The results of different methods are varied. But each method contributes to the revealing the transparence of spatial structure. Through the result of three kinds of classification, Chung-mu city which has special characteristics and the group of Industrial cities composed by Changwon, Ulsan, Masan, Chinhai, Kimhai, Yangsan, Ungsang, Changsungpo and Shinhyun are evident in common. Even though the appraisal of the fuzzy classification methods, this framework appears to be more realistic and flexible in preserving information pertinent to urban classification.

• Asia Marketing Journal
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• v.14 no.1
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• pp.1-24
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• 2012

Nowadays, customer satisfaction has been one of company's major objectives, and the index to measure and communicate customer satisfaction has been generally accepted among business practices. The major issues of CSI(customer satisfaction index) are three questions, as follows: (a)what level of customer satisfaction is tolerable, (b)whether customer satisfaction and company performance has positive causality, and (c)what to do to improve customer satisfaction. Among these, the second issue is recently attracting academic research in several perspectives. On this study, the second issue will be addressed. Many researchers including Anderson have regarded customer satisfaction as core competencies, such as brand equity, customer equity. They want to verify following causality "customer satisfaction → market performance(market share, sales growth rate) → financial performance(operating margin, profitability) → corporate value performance(stock price, credit ratings)" based on the process model of marketing performance. On the other hand, Insoo Jeon and Aeju Jeong(2009) verified sequential causality based on the process model by the domestic data. According to the rejection of several hypotheses, they suggested the balance model of marketing performance as an alternative. The objective of this study, based on the existing process model, is to examine the causal relationship between customer satisfaction and corporate value performance. Anderson and Mansi(2009) proved the relationship between ACSI(American Customer Satisfaction Index) and credit ratings using 2,574 samples from 1994 to 2004 on the assumption that credit rating could be an indicator of a corporate value performance. The similar study(Sangwoon Yoon, 2010) was processed in Korean data, but it didn't confirm the relationship between KCSI(Korean CSI) and credit ratings, unlike the results of Anderson and Mansi(2009). The summary of these studies is in the Table 1. Two studies analyzing the relationship between customer satisfaction and credit ratings weren't consistent results. So, in this study we are to test the conflicting results of the relationship between customer satisfaction and credit ratings based on the research model considering Korean credit ratings. To prove the hypothesis, we suggest the research model as follows. Two important features of this model are the inclusion of important variables in the existing Korean credit rating system and government support. To control their influences on credit ratings, we included three important variables of Korean credit rating system and government support, in case of financial institutions including banks. ROA, ER, TA, these three variables are chosen among various kinds of financial indicators since they are the most frequent variables in many previous studies. The results of the research model are relatively favorable : R², F-value and p-value is .631, 233.15 and .000 respectively. Thus, the explanatory power of the research model as a whole is good and the model is statistically significant. The research model has good explanatory power, the regression coefficients of the KCSI is .096 as positive(+) and t-value and p-value is 2.220 and .0135 respectively. As a results, we can say the hypothesis is supported. Meanwhile, all other explanatory variables including ROA, ER, log(TA), GS_DV are identified as significant and each variables has a positive(+) relationship with CRS. In particular, the t-value of log(TA) is 23.557 and log(TA) as an explanatory variables of the corporate credit ratings shows very high level of statistical significance. Considering interrelationship between financial indicators such as ROA, ER which include total asset in their formula, we can expect multicollinearity problem. But indicators like VIF and tolerance limits that shows whether multicollinearity exists or not, say that there is no statistically significant multicollinearity in all the explanatory variables. KCSI, the main subject of this study, is a statistically significant level even though the standardized regression coefficients and t-value of KCSI is .055 and 2.220 respectively and a relatively low level among explanatory variables. Considering that we chose other explanatory variables based on the level of explanatory power out of many indicators in the previous studies, KCSI is validated as one of the most significant explanatory variables for credit rating score. And this result can provide new insights on the determinants of credit ratings. However, KCSI has relatively lower impact than main financial indicators like log(TA), ER. Therefore, KCSI is one of the determinants of credit ratings, but don't have an exceedingly significant influence. In addition, this study found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size, and on service companies than manufacturers. The findings of this study is consistent with Anderson and Mansi(2009), but different from Sangwoon Yoon(2010). Although research model of this study is a bit different from Anderson and Mansi(2009), we can conclude that customer satisfaction has a significant influence on company's credit ratings either Korea or the United State. In addition, this paper found that customer satisfaction had more meaningful impact on corporations of small asset size than those of big asset size and on service companies than manufacturers. Until now there are a few of researches about the relationship between customer satisfaction and various business performance, some of which were supported, some weren't. The contribution of this study is that credit rating is applied as a corporate value performance in addition to stock price. It is somewhat important, because credit ratings determine the cost of debt. But so far it doesn't get attention of marketing researches. Based on this study, we can say that customer satisfaction is partially related to all indicators of corporate business performances. Practical meanings for customer satisfaction department are that it needs to actively invest in the customer satisfaction, because active investment also contributes to higher credit ratings and other business performances. A suggestion for credit evaluators is that they need to design new credit rating model which reflect qualitative customer satisfaction as well as existing variables like ROA, ER, TA.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.121-145
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• 2010

University timetabling depending on the educational environments of universities is an NP-hard problem that the amount of computation required to find solutions increases exponentially with the problem size. For many years, there have been lots of studies on university timetabling from the necessity of automatic timetable generation for students' convenience and effective lesson, and for the effective allocation of subjects, lecturers, and classrooms. Timetables are classified into a course timetable and an examination timetable. This study focuses on the former. In general, a course timetable for liberal arts is scheduled by the office of academic affairs and a course timetable for major subjects is scheduled by each department of a university. We found several problems from the analysis of current course timetabling in departments. First, it is time-consuming and inefficient for each department to do the routine and repetitive timetabling work manually. Second, many classes are concentrated into several time slots in a timetable. This tendency decreases the effectiveness of students' classes. Third, several major subjects might overlap some required subjects in liberal arts at the same time slots in the timetable. In this case, it is required that students should choose only one from the overlapped subjects. Fourth, many subjects are lectured by same lecturers every year and most of lecturers prefer the same time slots for the subjects compared with last year. This means that it will be helpful if departments reuse the previous timetables. To solve such problems and support the effective course timetabling in each department, this study proposes a university timetabling support system based on two phases. In the first phase, each department generates a timetable template from the most similar timetable case, which is based on case-based reasoning. In the second phase, the department schedules a timetable with the help of interactive user interface under the timetabling criteria, which is based on rule-based approach. This study provides the illustrations of Hanshin University. We classified timetabling criteria into intrinsic and extrinsic criteria. In intrinsic criteria, there are three criteria related to lecturer, class, and classroom which are all hard constraints. In extrinsic criteria, there are four criteria related to 'the numbers of lesson hours' by the lecturer, 'prohibition of lecture allocation to specific day-hours' for committee members, 'the number of subjects in the same day-hour,' and 'the use of common classrooms.' In 'the numbers of lesson hours' by the lecturer, there are three kinds of criteria : 'minimum number of lesson hours per week,' 'maximum number of lesson hours per week,' 'maximum number of lesson hours per day.' Extrinsic criteria are also all hard constraints except for 'minimum number of lesson hours per week' considered as a soft constraint. In addition, we proposed two indices for measuring similarities between subjects of current semester and subjects of the previous timetables, and for evaluating distribution degrees of a scheduled timetable. Similarity is measured by comparison of two attributes-subject name and its lecturer-between current semester and a previous semester. The index of distribution degree, based on information entropy, indicates a distribution of subjects in the timetable. To show this study's viability, we implemented a prototype system and performed experiments with the real data of Hanshin University. Average similarity from the most similar cases of all departments was estimated as 41.72%. It means that a timetable template generated from the most similar case will be helpful. Through sensitivity analysis, the result shows that distribution degree will increase if we set 'the number of subjects in the same day-hour' to more than 90%.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.9 no.2
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• pp.79-110
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• 1976

In Korea fermented fish and shellfish have traditionally been favored and consumed as seasonings or further processed for fish sauce. Three major items in production quantity among more than thirty kinds which are presently available in the market are fermented anchovy, oyster and small shrimp. They are usually used as a seasoning mixture of Kimchi in order to provide a distinctive flavor. Fermented small shrimp, Acetes chinensis is most widely and largely used ana occupies an important position in food industry of this country. But no study on its taste compounds has been reported. This study was attempted to establish the basic data for evaluating taste compounds of fermented small shrimp. The changes of such compounds during fermentation as free amino acids, nucleotides and their related compounds, TMAO, TMA, and betaine were analysed. In addition, change in microflora during the fermentation under the halophilic circumstance was also investigated. The samples were prepared with three different salt contents of 20, 30 and $40\%$ to obtain the proper degree of fermentation at a controlled tempeature of $20{\pm}2^{\circ}C$. The results are summarized as follows: Volatile basic nitrogen increased rapidly until 108 days of fermentation and afterwards it tended to increase slowly. Amino nitrogen also increased rapidly until 43 days of fermentation and then increased slowly. Extract nitrogen increased and marked the maximum value at 72 day fermentation and then decreased slowly. ADP, AMP and IMP tended to degrade rapidly while hypoxanthine increased remarkably at 27 day fermentation but slightly decreased at 72 day fermentation. It is presumed that the characteristic flavor of fermented small shrimp might be attributed to the relatively higher content of hypoxanthine. In the free amino acid composition of fresh small shrimp abundant amino acids were proline, arginine, alanine, glycine, lysine, glutamic acid, leucine, valine and threonine in order. Such amino acids like serine, methionine, isoleucine, phenylalanine, aspartic acid, tyrosine and histidine were poor. In small shrimp extract, proline, arginine, alanine, glycine, lysine and glutamic acid were dominant holding $18.5\%,\;14.6\%,\;10.8\%,\;8.7\%,\;8.1\%\;and\;7.7\%$ of total free amino acids respectively. The total free amino acid nitrogen in fresh small shrimp was $63.9\%$ of its extract nitrogen. The change of free amino acid composition in the extract of small shrimp during fermentation was not observed. Lysine, alanine glutamic acid, proline, glycine and leucine were abundant in both fresh sample and fermented products. The increase of total free amino acids during 72 day fermentation reached approximately more than 2 times as compared with that of fresh sample and then decreased slowly. Fermented small shrimp with $40\%$ of salt was too salty to be commercial quality as the results of organoleptic test showed. It is found that 72 day fermentation with $20\%\;and\;30\%$ of salt gave the most favorable flavor. It is convinced that the characteristic flavor of fermented small shrimp was also attributed to such amino acids as lysine, proline, alanine, glycine and serine known as sweet compounds, as glutamic acid with meaty taste, and as leucine known as bitter taste. The amount of betaine increased during fermentation and reached the maximum at 72 day fermentation and then decreased slowly TMA increased while TMAO decreased during fermentation. The amount of TMAO nitrogen in fermented small shrimp was $200mg\%$ on moisture and salt free base. Betaine and TMAO known as sweet compounds were abundant in fermented small shrimp. It is supposed that these compounds could also play a role as important taste compounds of fermented small shrimp. At the initial stage of fermentation, Achromobacter, Pseudomonas, Micrococcus denitrificans which belong to marine bacteria were isolated. After 40 day fermentation, they disappeared rapidly while Halabacterium, Pediococcus, Sarcian, Micrococcus morrhuae and the yeasts such as Saccharomyces sp. and Torulopsis sp. dominated. It is concluded that the most important taste compounds of fermented small shrimp were amino acids such as lysine, proline, alanine, glycine, serine, glutamic acid, and leucine, betaine, TMAO and hypoxanthine.

• Asia Marketing Journal
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• v.11 no.3
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• pp.31-56
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• 2009

As the competition intensifies and the market matures, marketers are more and more concerned with the relationship marketing. Many of the previous researches have pointed out that not all of the consumers are relationship-oriented. But none of the previous research has systematically investigated this issue. This research investigated the relationship among the three concepts: consumers' intrinsic characteristics, perceived importance of relational benefits, and relationship building intention with the salesperson. In this research the perceived importance of relational benefits is treated as mediating variable in the relationship between consumers' intrinsic characteristics and relationship building intention with the salesperson. The conceptual model in this study can be depicted as follows. From the consumers' perspective relational benefits can be defined as "the additional benefits consumers can receive in addition to core services through the long-term relationship with the service provider." And in this study two kinds of relational benefits are adopted by reviewing the previous research: confidence benefits and social benefits. Relational benefit received from the salesperson is very important to predict consumers' relationship building intention with the salesperson. The more relational benefits consumer wants from the salesperson, the more relationship building intention he/she has. From this point two hypotheses are derived as follows. Hypothesis 1: As the perceived importance of confidence benefit from the salesperson increases, the relationship building intention with the salesperson increases. Hypothesis 2: As the perceived importance of social benefit from the salesperson increases, the relationship building intention with the salesperson increases. In this study four individual characteristics(risk taking tendency, variety-seeking tendency, product knowledge, trust orientation) are hypothesized to influence the perceived importance of confidence benefits from the salesperson. And three individual characteristics(interpersonal orientation, price consciousness, trust orientation) are hypothesized to influence the perceived importance of social benefits from the salesperson. These 7 hypotheses are as follows. Hypothesis 3: As the risk taking tendency increases, the perceived importance of confidence benefits from the salesperson decreases. Hypothesis 4: As the variety-seeking tendency increases, the perceived importance of confidence benefits from the salesperson decreases. Hypothesis 5: As the product knowledge increases, the perceived importance of confidence benefits from the salesperson decreases. Hypothesis 6: As the trust orientation increases, the perceived importance of confidence benefits from the salesperson increases. Hypothesis 7: As the interpersonal orientation increases, the perceived importance of social benefits from the salesperson increases. Hypothesis 8: As the price consciousness increases, the perceived importance of social benefits from the salesperson decreases. Hypothesis 9: As the trust orientation increases, the perceived importance of social benefits from the salesperson increases. The whole model in this study can be depicted as follows: Data were collected from the 396 consumers who actually trade stocks through the salesperson and were analyzed using structural equation model. The analysis results show that consumers' perceived importance of relational benefits(confidence benefit and social benefit) play the roles of mediating variables in the causal relationship between consumers' inherent characteristics and their relationship building intention with the salesperson. As for the individual characteristics, the influences of variety-seeking tendency, trust orientation, and price consciousness are statistically significant. It was found that variety-seeking tendency has a significant negative effect on the perceived importance of confidence benefit, and that trust orientation has a significant positive effect on the perceived importance of both of confidence and social benefit. Finally it was also found that, on the contrary to the influence direction suggested in the hypothesis, price consciousness has a significant positive effect on the perceived importance of social benefit.