• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.121-131
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• 2023

The Chinese three northeastern three provinces - Heilongjiang, Liaoning, and Jilin - hold significant geographical, geopolitical, and commercial importance due to their location allowing for cross-border trade and transportation with North Korea. These provinces are crucial for establishing a complex Eurasian logistics network in line with South Korea's new northern policy. The Chinese three northeastern three provinces, as this region is known, boasts excellent maritime transportation links between South Korea, China, and North Korea, making it an logistics hub for transporting goods to Eurasia and Europe through multimodal transport. This study highlights the importance of securing a logistics hub area by fostering cooperation and friendly relations with China's three northeastern three provinces, which are crucial to the success of the New Northern Policy. In particular, the study aims to analyze current status of trade with these region and freight volume transported by ships and recommend political advice for securing logistics hub and revitalizing maritime transport. As the policy suggestion, this study is to establish a logistics hub by implementing joint port operations, constructing port infrastructure jointly, and operating shipping companies together. Additionally, we propose ways to revitalize the maritime passenger transport business between Korea and China, which involves expanding cultural exchanges and developing content.

• 한국노년학
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• v.29 no.1
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• pp.195-213
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• 2009

The purpose of this study is to analyse how consumption value affects the loyalty of green product through satisfaction and trust among elderly consumers. Data were collected from a cross-sectional survey of 314 older adults (age≥60) in the U. S., who bought and possessed a hybrid car, a representative green product. The statistical methodology is employed a structural equation model. The results demonstrated several important findings. First, perceived social value among elderly population had significant effects on green product satisfaction, while hedonic value did not. Second, both perceived functional value and environment friendly value had a significant positive effect on trust in green products. Third, satisfaction with green products also led to trust in green products. Finally, trust in green products showed their significant effects on loyalty in green product. These results provide practical implications to improve the trust and loyalty in green products among the elderly consumers. Furthermore, by deriving major components of consumption values in green products among the elderly, and analyzing the mechanism of satisfaction, trust, and loyalty, the study emphasizes relationship marketing in implementing "green" marketing strategies.

• Annals of Occupational and Environmental Medicine
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• v.35
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• pp.39.1-39.14
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• 2023

Background: Diseases affecting the lungs and airways contribute significantly to the global burden of disease. The problem in low- and middle-income countries appears to be exacerbated by a shift in global manufacturing base to these countries and inadequate enforcement of environmental and safety standards. In Ghana, the potential adverse effects on respiratory function associated with occupational wood dust exposure have not been thoroughly investigated. Methods: Sixty-four male sawmill workers and 64 non-woodworkers participated in this study. The concentration of wood dust exposure, prevalence and likelihood of association of respiratory symptoms with wood dust exposure and changes in pulmonary function test (PFT) parameters in association with wood dust exposure were determined from dust concentration measurements, symptoms questionnaire and lung function test parameters. Results: Sawmill workers were exposed to inhalable dust concentration of 3.09 ± 0.04 mg/m³ but did not use respirators and engaged in personal grooming habits that are known to increase dust inhalation. The sawmill operators also showed higher prevalence and likelihoods of association with respiratory symptoms, a significant cross-shift decline in some PFT parameters and a shift towards a restrictive pattern of lung dysfunction by end of daily shift. The before-shift PFT parameters of woodworkers were comparable to those of non-woodworkers, indicating a lack of chronic effects of wood dust exposure. Conclusions: Wood dust exposure at the study site was associated with acute respiratory symptoms and acute changes in some PFT parameters. This calls for institution and enforcement of workplace and environmental safety policies to minimise exposure at sawmill operating sites, and ultimately, decrease the burden of respiratory diseases.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.318-332
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• 2015

The maize production in South-eastern Asian countries showed a continuous increase with increasing poultry-livestock from the beginning of the 1990s to early 2010. Also the need for a new variety development of each contries was increased rapidly in the same period. Single-Cross hybrid varieties have been developed and supplied from 2001 instead of multi-cross maize varieties since 1992 in Indonesia. In Cambodia, CP group is mainly manufacturing feeds with most of the forage maize from farmers who are growing its seeds from the company. Cambodian main cultivars are varieties of multinational corporations such as DK8868 from Monsanto, NK6326, NK7328 from Syngenta and CP333 from CP group including local business company. Vietnam is the main maze importing country in South-Eastern Asia which had imported 13 times scale of amount compared to exports in average from 1990 to 2011. Vietnamese government has developed a range of varieties for improving their efficiency in production, such as the LVN-10 with political investments. Their production has been reached to 80% of the total. According to the 2012 MIFAFF (Ministry for Food, Agriculture, Forestry and Fisheries) data in Korea, domestic edible maize cultivation area was approximately 15,000ha. It showed 74,399 tons of production, 3.8% of food self-sufficiency in maize and around 0.9% of grain self-sufficiency rate. The consumption of grain is mostly rely on imports in Korea. To overcome the limit of the domestic seed market and increase maize self-sufficiency, the need to develop maze varieties for world-class is increasing at present through analyzing the market trend and prospect of the seed industry in South-eastern Asia.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.164-172
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• 2010

This study reviews the influence of culture in interpersonal servicescapes by examining the restaurant retail setting. Two cultures (Canada and France) are surveyed in order to better understand their retail expectations towards interpersonal servicescapes. Using Hofstede's (1991) cultural dimensions to explain some of the differences between Canadian and French restaurant patrons, this study demonstrates a potentially interesting research avenue in the field of cross-cultural interpersonal services marketing. It demonstrates that cultural dimensions do not operate independently but interdependently. Understanding this can help retailers better explain complex service interactions between countries that may appear similar in terms of various socio-demographic features. In this exploratory research, a measure via exploratory factor analysis was developed, one that encompasses both the physical and service aspects common to interpersonal servicescape by using personality traits. This measure was tested in order to better understand the service expectations between two cultures, Canada and France. Five dimensional structures were uncovered in both cultures but with different traits and groupings. The differences between the traits uncovered and the overall Canadian and French personality structures find some explanation using Hofstede's (1991) cultural dimensions. The results of this survey point to a possible explanation as to why when services are transferred between cultures, the perceptions of them can be different and sometimes even lead to service failure. There are clearly some cultural differences between the Canadian and French consumers and their overall expectations regarding their consumption experience. Reviewing the first factor of the French and Canadian personality structures shows that the individualist/collectivist differences are apparent between the Canadian and the French cultures. The second dimension also has quite a few traits in common, five, all of which have the personal treatment aspect of the restaurant experience that a service provider would be responsible for: polite, respectful, and dedicated. Notable is that the French dimension does not include the authenticity or the hospitable aspect of the experience but includes even more features that are inherent to the personal interaction, such as charming and courteous. The third dimension of the Canadian and French structures reflects completely different expectations. Whereas the French dimension centers around energy and enthusiasm, the Canadian version is more laid-back and relaxed. There is extroversion in the French dimension to introversion in the Canadian dimension. This could be explained by differences on the Uncertainty Avoidance dimension as outlined by Hofstede (1991). The fourth dimension seems to confirm previously outlined cultural differences. Whereas Canadians, being a bit lower on uncertainty avoidance and power distance, prefer an intimate and private experience, the French continue to expect extraversion and inclusive features to their experience. The fifth dimension is in the French personality structure a clear expression of the high power distance society, where the roles of the players in the restaurant experience are clearly defined and the rules of engagement preserved. This study demonstrates that different cultures clearly do relate to different expectations regarding interpersonal services. This is apparent in the dimensions that come up in both the French and the Canadian personality structures, not only in terms of how different they are but also in with which cultural dimensions these can be explained. For interpersonal servicescapes, the use of personality traits is interesting as it allows for both physical and service features to be accounted for. Furthermore, the social component inherent to interpersonal servicescapes surfaces in most of the dimensions of the service personality structures. The quality of social exchanges is extremely important, and this even more so in cross-cultural situations, where the expec tations regarding the service experience may vary. As demonstrated by this research and using Hofstede's (1991) paradigm, not all societies will have the same expectations pertaining to the interpersonal services. Furthermore, the traditions surrounding the type of service can also have an impact on the service evaluations and differ between countries and cultures. However, using personality traits may also allow for retailers to see which service traits are common to two or more cultures where they seek to be present, and focus on these in the offering. The findings demonstrate the importance of the individualist and collectivist dimension for interpersonal servicescapes. This difference between the French and the Canadian personality structure is apparent in the most dominant dimension as well as within others. The findings are a step in explaining how retailers can transfer and then measure interpersonal services across cultures.

• Asia pacific journal of information systems
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• v.19 no.4
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• pp.47-75
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• 2009

Recently, researches on Management Information Systems (MIS) have laid out theoretical foundation and academic paradigms by introducing diverse theories, themes, and methodologies. Especially, academic paradigms of MIS encourage a user-friendly approach by developing the technologies from the users' perspectives, which reflects the existence of strong causal relationships between information systems and user's behavior. As in other areas in social science the use of structural equation modeling (SEM) has rapidly increased in recent years especially in the MIS area. The SEM technique is important because it provides powerful ways to address key IS research problems. It also has a unique ability to simultaneously examine a series of casual relationships while analyzing multiple independent and dependent variables all at the same time. In spite of providing many benefits to the MIS researchers, there are some potential pitfalls with the analytical technique. The research objective of this study is to provide some guidelines for an appropriate use of SEM based on the assessment of current practice of using SEM in the MIS research. This study focuses on several statistical issues related to the use of SEM in the MIS research. Selected articles are assessed in three parts through the meta analysis. The first part is related to the initial specification of theoretical model of interest. The second is about data screening prior to model estimation and testing. And the last part concerns estimation and testing of theoretical models based on empirical data. This study reviewed the use of SEM in 164 empirical research articles published in four major MIS journals in Korea (APJIS, ISR, JIS and JITAM) from 1991 to 2007. APJIS, ISR, JIS and JITAM accounted for 73, 17, 58, and 16 of the total number of applications, respectively. The number of published applications has been increased over time. LISREL was the most frequently used SEM software among MIS researchers (97 studies (59.15%)), followed by AMOS (45 studies (27.44%)). In the first part, regarding issues related to the initial specification of theoretical model of interest, all of the studies have used cross-sectional data. The studies that use cross-sectional data may be able to better explain their structural model as a set of relationships. Most of SEM studies, meanwhile, have employed. confirmatory-type analysis (146 articles (89%)). For the model specification issue about model formulation, 159 (96.9%) of the studies were the full structural equation model. For only 5 researches, SEM was used for the measurement model with a set of observed variables. The average sample size for all models was 365.41, with some models retaining a sample as small as 50 and as large as 500. The second part of the issue is related to data screening prior to model estimation and testing. Data screening is important for researchers particularly in defining how they deal with missing values. Overall, discussion of data screening was reported in 118 (71.95%) of the studies while there was no study discussing evidence of multivariate normality for the models. On the third part, issues related to the estimation and testing of theoretical models on empirical data, assessing model fit is one of most important issues because it provides adequate statistical power for research models. There were multiple fit indices used in the SEM applications. The test was reported in the most of studies (146 (89%)), whereas normed-test was reported less frequently (65 studies (39.64%)). It is important that normed- of 3 or lower is required for adequate model fit. The most popular model fit indices were GFI (109 (66.46%)), AGFI (84 (51.22%)), NFI (44 (47.56%)), RMR (42 (25.61%)), CFI (59 (35.98%)), RMSEA (62 (37.80)), and NNFI (48 (29.27%)). Regarding the test of construct validity, convergent validity has been examined in 109 studies (66.46%) and discriminant validity in 98 (59.76%). 81 studies (49.39%) have reported the average variance extracted (AVE). However, there was little discussion of direct (47 (28.66%)), indirect, and total effect in the SEM models. Based on these findings, we suggest general guidelines for the use of SEM and propose some recommendations on concerning issues of latent variables models, raw data, sample size, data screening, reporting parameter estimated, model fit statistics, multivariate normality, confirmatory factor analysis, reliabilities and the decomposition of effects.

• Knowledge Management Research
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• v.22 no.4
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• pp.71-85
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• 2021

The purpose of this study is to find out which artificial intelligence methodology is most suitable for creating a foreign exchange rate prediction model using the indicators of bond market and interest rate market. KTBs and MSBs, which are representative products of the Korea bond market, are sold on a large scale when a risk aversion occurs, and in such cases, the USD/KRW exchange rate often rises. When USD liquidity problems occur in the onshore Korean market, the KRW Cross-Currency Swap price in the interest rate market falls, then it plays as a signal to buy USD/KRW in the foreign exchange market. Considering that the price and movement of products traded in the bond market and interest rate market directly or indirectly affect the foreign exchange market, it may be regarded that there is a close and complementary relationship among the three markets. There have been studies that reveal the relationship and correlation between the bond market, interest rate market, and foreign exchange market, but many exchange rate prediction studies in the past have mainly focused on studies based on macroeconomic indicators such as GDP, current account surplus/deficit, and inflation while active research to predict the exchange rate of the foreign exchange market using artificial intelligence based on the bond market and interest rate market indicators has not been conducted yet. This study uses the bond market and interest rate market indicator, runs artificial neural network suitable for nonlinear data analysis, logistic regression suitable for linear data analysis, and decision tree suitable for nonlinear & linear data analysis, and proves that the artificial neural network is the most suitable methodology for predicting the foreign exchange rates which are nonlinear and times series data. Beyond revealing the simple correlation between the bond market, interest rate market, and foreign exchange market, capturing the trading signals between the three markets to reveal the active correlation and prove the mutual organic movement is not only to provide foreign exchange market traders with a new trading model but also to be expected to contribute to increasing the efficiency and the knowledge management of the entire financial market.

• Journal of Fashion Business
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• v.3 no.1
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• pp.65-80
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• 1999

In this study, the researcher studied the historical background and the traditional culture about dress and ornament of Yunnan Province of China. The Results of the study are as follows. 1. In the Past, Derung's dress was very simple due to the influence of various factors, such as geography and history. Men wore shorts and covered diagonally a piece of cloth from left shoulders to right armpits and tied up the two ends on chests. Women covered crisscross two pieces of cloth from both shoulders to knee. 2. Achang people's dress and adornment has its own unique characteristic. Generally, men wear Jackets with buttons down the front and black trousers. Unmarried men like to wear white turbans, while most of married men usually wear dark blue ones. Women usually wear tight-sleeve blouses with buttons down the front and skirts. Unmarried women wear the hair in braids coil them on the top of their heads. They wear short blouses and trousers. Married women wear their hair Into buns and like to entwine black or blue cloth into high trubans. They wear short blouses and knee-length straight skirts. Achang knife enjoys high reputation and has a long history and an exquisite workmanship. All the men like to wear it. 3. The dress and adornment of the Lahu nationality has both the characteristic of farming culture and the style of nomadic culture of early times. Men usually wear short shirts with round necks and buttons down the front, loose-legged trousers, turbans or dark blue cloth caps Women's dress and adornment can be categorized into two styles. One is black cloth gown with buttons diagonally on the right front and waist-length slits on both sides. The edges of fronts and cuffs are edged with Silver ornaments and lace. They also wear trousers. The other is short blouse with round neck and short opening on th right front, straight skirt and colourful leggings with embroidered patterns. 4. The Hani people, men and women, old and young, like black colour and are fond of wearing black clothes. Men usually wear shirts with buttons down the front and trousers, entwining their heads with black or white cloth. The elderly people wear calottes. Women wear cloth blouses, skirts and trousers or shorts. Slight differences exist in the clothing and adornments according to region, branch and age 5. Blang people's dyeing technique with an exquisite method has a long history. Men wear dark blue long sleeve shirts with round necks and buttons down the front or arranged diagonally on the front and loose-legged trousers. Elderly men wear big turbans wdress and adornment varies greatly in different regions. 6. The Lisu people culture of dress and adornment has some unique characteristics. The styles and colours of their dress and adornment differ slightly from place to place. In the Nujiang area, Women wear black velvet Jackets over blouses with buttons arranged diagonally on the right front and long pleated ramie skirts. Men usually wear wraparound ramie gowns, with center vent, made of fabrics alternated with white and black cross stripes. They also wear cloth waistbands and trohile youngsters keep their hair short. Women's users. In the Lushui area, the dress and adornment is similar to that in the Nujing region, but women wear aprons and trousers instead of skirts. 7. The Nu people dress and adornment is simple but elegant Women are proficient in ramie-weaving. Men usually wear gowns With overlapping necks, knee-length trousers and leggings. They like to wear their hair long and entwine dark blue or white turbans. Women wear black and red vests over blouses with buttons arranged diagonally on the right front and ankle-length skirts. They also wear their hair long, make it into braids, and entwine dark blue or colourful cloth turbans. 8. Pumi men usually wear ramie shirts With buttons arranged diagonally on the right front, loose trousers and white sheepskin vests. Some also wear overcoats made of "pulu". Women's dress and adornment varies in different areas. In the Lanping and Weixi regions, women wear white short blouses with buttons arranged diagonally on the front and dark brown embroidered vests. They also wear trousers and blue or black cloth turbans. In the Ninglang and Yongsheng regions, women wear hemmed blouses With buttons arranged diagonally on the right front and drape sheepskin capes. They also wear white pleated skirts and use broad colourful cloth as their waistbands.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.