• Journal of the Korean Society of International Agriculture
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• v.23 no.5
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• pp.537-545
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• 2011

This study was conducted to obtain basic information on physiological and proteomic responses of barley seedlings to salt stress. Shoot dry weight decreased significantly as the level of soil salinity increased. Salt stress-induced decrease of relative shoot dry weight was lower in cv. "Sanglok" than in cv. "Sunwoo". Under the salt stress, SPAD value decreased, and the value was higher in cv. "Sanglok" than in cv. "Sunwoo". Sodium ion content in the leaves increased as NaCl concentration increased, and the content was higher in cv. "Sunwoo" than in cv. "Sanglok". The K⁺/Na⁺ ratio was higher in cv. "Sanglok" than in cv. "Sunwoo". Salt stress-induced alterations in protein expression of the leaves were detected by two dimensional electrophoresis, and 47 protein spots showing altered expression were selected. Among the selected protein spots, 17 protein spots were up-regulated and 28 spots down-regulated in cv. "Sanglok". In cv. "Sunwoo", 14 protein spots were up-regulated and 27 spots down-regulated. Out of 47 deferentially expressed protein spots, 18 protein spots were identified using mass spectrometry and NCBI protein database. Among the identified proteins, ten proteins are known to be involved in various stress responses, but the others are not directly involved in stress responses.

• Asia Marketing Journal
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• v.12 no.4
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• pp.165-181
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• 2011

In 2005, Yuhan Kimberly found interesting points in the existing customers' U&A survey on the kitchen towel. The usage of Korean consumers is usually restricted to getting rid of the oil from the fans and the fried food. This usage limits could be the barrier to the diffusion of kitchen towel. Although consumers were worried about the hygiene situations about the dishcloth, they also percieved that the existing paper kitchen towels were short of something to soothe their inconveniences. As a result, the company made a decision to seek out the solution for the consumers' worries. The relative shortage of the paper kitchen towel compared to that of the unhygienic and inconvenient dishcloth was its lack of water-endurance. The dishcloth could be reliable in the wet environment which is very common in Korean kitchen, whereas the paper kitchen towel was perceived as very weak and unreliable in removing water form the dishes and the sink. To overcome the common sense of the consumers, it is important to shift the consumers' perception of the kitchen towel category. It is needed to expand the usage time from one time to several times in a day. So it is needed to redesign the customers' kitchen life. The company adopted the brand "Scott^®" to meet the global brand strategy of the parent company, Kimberley Clark. This brand was also adopted and made a succesful launch of the similar product lines in Latin America. Furthermore, to make an emphasis on the differentiation from the existing paper kitchen towels, the company made the slogan, "Scott^® washable kitchen towel." This slogan was designed to expand the familiar product image of convenient paper towels to water-resistance. As a result, consumers show the changes in usage behavior of paper towels and apply them for more various purposes such as cleaning the decks and tables. This change results in the rapid sales increase of "Scott^® washable kitchen towel."

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.398-403
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• 2023

Process-based K-cabbage model is based on physiological processes such as photosynthesis and phenology, making it possible to predict crop growth under different climate conditions that have never been experienced before. Current first-stage process-based models can be used to assess climate impact through yield prediction based on climate change scenarios, but no comparison has been performed between big data obtained from the main production area and model prediction so far. The aim of this study was to find out the direction of model improvement when using the current model for yield prediction. For this purpose, model performance evaluation was conducted based on data collected from farmers growing 'Chungwang' cabbage in Taebaek and Samcheok, the main producing areas of Chinese cabbage in highland region. The farms surveyed in this study had different cultivation methods in terms of planting date and soil water and nutrient management. The results showed that the potential biomass estimated using the K-cabbage model exceeded the observed values in all cases. Although predictions and observations at the time of harvest did not show a complete positive correlation due to limitations caused by the use of fresh weight in the model evaluation process (R²=0.74, RMSE=866.4), when fitting the model based on the values 2 weeks before harvest, the growth suitability index was different for each farm. These results are suggested to be due to differences in soil properties and management practices between farms. Therefore, to predict attainable yields taking into account differences in soil and management practices between farms, it is necessary to integrate dynamic soil nutrient and moisture modules into crop models, rather than using arbitrary growth suitability indices in current K-cabbage model.

• Journal of The Korean Society of Grassland and Forage Science
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• v.29 no.3
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• pp.171-178
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• 2009

The study was conducted to clarify the mixed seeding rate of whole crop barley with hood type and forage pea for using of forage crops and to compare the forage yield and quality. At a mixed seeding rate between the whole crop barley (WCB) and forage pea, The heading date and plant height of WCB were not a difference according to mixed seeding rate of forage pea. The tillers of the WCB were a decrease and plant of the forage pea were a increase according to increased seeding rate of forage pea. The lodging index of the WCB was a appearance with distribution of $0{\sim}3$, The lodging index of WCB with a 20kg/10a seeding rate of a only WCB without seeding of the forage pea was 3. The overwintering rate of forage pea was a appearance more than 90% at all treatment. The plant height of forage pea was a increase according to increased seeding rate of forage pea at 14 kg/10a and 20 kg/10a plots of WCB. At a mixed seeding between the WCB and forage pea, The fresh weight was a increase according to increased seeding rate of forage pea and was a appearance more than 3,000 kg at all treatment plot. But the dry matter weight was decrease according to increased seeding rates of forage pea. The dry matter weight of 20 kg/10a seeding rate of a only WCB without seeding of the forage pea showed the most amount with 1,266 kg. The crude protein (CP) content was a tendency to increase according to increased seeding rates of forage pea. But, the relative feed value (RFV) was a tendency to decrease according to increased seeding rate of forage pea. The highest RFV was 183.8 at 14 kg/10a seeding rate of a only WCB without seeding of the forage pea. The acid detergent fiber (ADF) and neutral detergent fiber (NDF) were a increase according to increased seeding rate of forage pea at 14 kg/10a and 20 kg/10a plots of WCB. The highest content of ADF and NDF were 23.9% and 46.3% at mixed seeding rate of 20 kg/10a of WCB with 10 kg/10a of forage pea, respectively. The highest sum of standardized score by fresh weight, dry matter weight, CP, ADF, NDF and RFV was 2.309 at mixed seeding rate of 20 kg/10a of WCB with 7.5 kg/10a of forage pea. The optimum mixed seeding rate was a considered judgment in the order of mixed seeding rate of 20 kg/10a of WCB with 7.5 kg/10a of forage pea, mixed seeding rate of 20 kg/10a of WCB with 5.0 kg/10a of forage pea.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of Nutrition and Health
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• v.42 no.8
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• pp.770-783
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• 2009

The school foodservice was quantitatively extended by policy of government all the while. There was carried out the survey of customer satisfaction about school foodservice by the ministry of education, science, and technology since 2006 years. Therefore, the purpose of this study was to grasp an improvement of the scores of school foodservice' quality attributes and satisfaction as compared with the preceding year by respondents and school type (elementary school, middle school, and high school). An annual survey was practiced to respondents (students, parents, and faculty) on september 2007 years and 2008 years in 16 cities and provinces. The statistics was analyzed to descriptive analysis and t-test for SPSS 12.0. The scores of school foodservice' quality attributes and overall customer satisfaction were almost increased to students, parents, and faculty and especially, big elevation in middle school. There was big increased the quality attributes such as 'providing information on foodservice', 'pleasant foodservice environment', 'kindness offered by employee' in elementary school, middle school, and high school to total respondents. An overall satisfaction in school foodservice was improved from 69.2 score to 71.9 score. On students, scores of overall satisfaction was increased from 72.9 to 74.0 as students of elementary school and from 61.5 to 65.8 as students of middle school (p < .001). Therefore, for improvement and development of school foodservice, there should be a necessary for an operator of school foodservice and an office of education to make an effort.

• Asia pacific journal of information systems
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• v.22 no.1
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• pp.29-52
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• 2012

Consumers differ in the way they make a purchase. An audio mania would willingly make a bold, yet serious, decision to buy a top-of-the-line home theater system, while he is not interested in replacing his two-decade-old shabby car. On the contrary, an automobile enthusiast wouldn't mind spending forty thousand dollars to buy a new Jaguar convertible, yet cares little about his junky component system. It is product involvement that helps us explain such differences among individuals in the purchase style. Product involvement refers to the extent to which a product is perceived to be important to a consumer (Zaichkowsky, 2001). Product involvement is an important factor that strongly influences consumer's purchase decision-making process, and thus has been of prime interest to consumer behavior researchers. Furthermore, researchers found that involvement is closely related to perceived risk (Dholakia, 2001). While abundant research exists addressing how product involvement relates to overall perceived risk, little attention has been paid to the relationship between involvement and different types of perceived risk in an electronic commerce setting. Given that perceived risk can be a substantial barrier to the online purchase (Jarvenpaa, 2000), research addressing such an issue will offer useful implications on what specific types of perceived risk an online firm should focus on mitigating if it is to increase sales to a fullest potential. Meanwhile, past research has focused on such consumer responses as information search and dissemination as a consequence of involvement, neglecting other behavioral responses like online merchant selection. For one example, will a consumer seriously considering the purchase of a pricey Guzzi bag perceive a great degree of risk associated with online buying and therefore choose to buy it from a digital storefront rather than from an online marketplace to mitigate risk? Will a consumer require greater trust on the part of the online merchant when the perceived risk of online buying is rather high? We intend to find answers to these research questions through an empirical study. This paper explores the impact of enduring product involvement and perceived risks on required trust level, and further on online merchant choice. For the purpose of the research, five types or components of perceived risk are taken into consideration, including financial, performance, delivery, psychological, and social risks. A research model has been built around the constructs under consideration, and 12 hypotheses have been developed based on the research model to examine the relationships between enduring involvement and five components of perceived risk, between five components of perceived risk and required trust level, between enduring involvement and required trust level, and finally between required trust level and preference toward an e-tailer. To attain our research objectives, we conducted an empirical analysis consisting of two phases of data collection: a pilot test and main survey. The pilot test was conducted using 25 college students to ensure that the questionnaire items are clear and straightforward. Then the main survey was conducted using 295 college students at a major university for nine days between December 13, 2010 and December 21, 2010. The measures employed to test the model included eight constructs: (1) enduring involvement, (2) financial risk, (3) performance risk, (4) delivery risk, (5) psychological risk, (6) social risk, (7) required trust level, (8) preference toward an e-tailer. The statistical package, SPSS 17.0, was used to test the internal consistency among the items within the individual measures. Based on the Cronbach's ${\alpha}$ coefficients of the individual measure, the reliability of all the variables is supported. Meanwhile, the Amos 18.0 package was employed to perform a confirmatory factor analysis designed to assess the unidimensionality of the measures. The goodness of fit for the measurement model was satisfied. Unidimensionality was tested using convergent, discriminant, and nomological validity. The statistical evidences proved that the three types of validity were all satisfied. Now the structured equation modeling technique was used to analyze the individual paths along the relationships among the research constructs. The results indicated that enduring involvement has significant positive relationships with all the five components of perceived risk, while only performance risk is significantly related to trust level required by consumers for purchase. It can be inferred from the findings that product performance problems are mostly likely to occur when a merchant behaves in an opportunistic manner. Positive relationships were also found between involvement and required trust level and between required trust level and online merchant choice. Enduring involvement is concerned with the pleasure a consumer derives from a product class and/or with the desire for knowledge for the product class, and thus is likely to motivate the consumer to look for ways of mitigating perceived risk by requiring a higher level of trust on the part of the online merchant. Likewise, a consumer requiring a high level of trust on the merchant will choose a digital storefront rather than an e-marketplace, since a digital storefront is believed to be trustworthier than an e-marketplace, as it fulfills orders by itself rather than acting as an intermediary. The findings of the present research provide both academic and practical implications. The first academic implication is that enduring product involvement is a strong motivator of consumer responses, especially the selection of a merchant, in the context of electronic shopping. Secondly, academicians are advised to pay attention to the finding that an individual component or type of perceived risk can be used as an important research construct, since it would allow one to pinpoint the specific types of risk that are influenced by antecedents or that influence consequents. Meanwhile, our research provides implications useful for online merchants (both online storefronts and e-marketplaces). Merchants may develop strategies to attract consumers by managing perceived performance risk involved in purchase decisions, since it was found to have significant positive relationship with the level of trust required by a consumer on the part of the merchant. One way to manage performance risk would be to thoroughly examine the product before shipping to ensure that it has no deficiencies or flaws. Secondly, digital storefronts are advised to focus on symbolic goods (e.g., cars, cell phones, fashion outfits, and handbags) in which consumers are relatively more involved than others, whereas e- marketplaces should put their emphasis on non-symbolic goods (e.g., drinks, books, MP3 players, and bike accessories).

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.1
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• pp.76-82
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• 2010

Experiments were conducted to investigate seed persistence of Chinese milk vetch(CMV) in naturally reseeded rice field in 2007~2009. The seed and pods with seeds were buried in rice field at 0, 5 and 10 cm depths and retrieved from the field at one to three month intervals from July to the following year March and determined change of seed dormancy and viability. In the second experiment, persistence of the CMV seeds in the naturally reseeded rice field at different tillage methods and soil depths were also investigated after rice harvest in autumn. Burial depths and durations affected recovery rate, dormancy and viability of CMV seed. The viability loss was faster and greater in the seed than the pod with seeds and on the soil surface than the 5 or 10 cm burial depths. The recovery rate of CMV seed was decreased starting from one month as seed burial and it was significantly decreased to 52~65% for the seed in September. However, unlike the seed burial, the nearly 100% CMV seeds were recovered for burial as pod with seeds even after four months burial in both 0 and 5 cm depths. However, the recovery rate was sharply declined to below 30% at October in 2007 in both seed and pods with seeds and in the 2008/2009 experiment. the 15~47% of CMV seeds still remained even after October. The CMV had high seed dormancy of 95%, showing only 4~5% germination at the beginning in June but the seed germination increased to 25 to 35% in seed and 55 to 61% in pod with seeds in September due to breakage of hard seed dormancy. The viability loss was faster in the seed than in the pod with seeds regardless of depths of placement in the soil base on decayed seeds. Also the seed placed on the soil surface lost viability faster than the 5~10 burial depths. On the other hand, field observation in the naturally reseeded CMV rice field showed that as many as 917~2,185 CMV seeds $m^2$ were from the 0~15 cm soil depth in the rotary tillage and 250~10,105 CMV seeds in minimum tillage treatmints. The recovered seed germinated 25~33%, 23~43% but still had high percentage of hard seed having 64~72% and 51~77% even after rice harvest in autumn. These results indicate that freshly harvested CMV seeds had high level of primary dormancy and the dormancy was gradually broken in soil with time during rice cultivation periods and appreciable number of CMV seeds remained even 4 month after burial in soil. CMV plant regenerated naturally from the remained seed bank at rice harvest time in autumn. The CMV seedling still emerged even after 2 years of continuous destructive killing of emerged CMV plant by rotary tillage in naturally reseeded CMV plant in rice field, indicating that CMV seeds do persistent as least two years in soil.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used