• Korean Journal of Soil Science and Fertilizer
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• v.36 no.3
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• pp.134-144
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• 2003

A rhizobacterium Pseudomonas cholororaphis O6 produced several secondary metabolites, such as phenazines, protease, and HCN that may be involved in inhibition of the growth of phytopathogenic fungi. In field study, P. chlororaphis O6 treatment on wheat seed suppressed root rot disease caused by Fusarium culmorum. The major organic acids of cucumber root exudates were fumaric acid, malic acid, benzoic acid, and succinic acid. Glucose and fructose were major monosaccharides in cucumber root exudates. The total amount of organic acids was ten times higher than that of the sugars. P. chlororaphis O6 grew well on cucumber root exudates. The dctA gene of P. chlororaphis O6 consisted of a 1,335 bp open reading frame with a deduced amino acid sequence of 444 residues, corresponding to a molecular size of about 47 kD and pI 8.2. The deduced dctA sequence has ten putative transmembrane domains, as expected of a membrane-embedded protein. Our results indicated that organic acids in cucumber root exudates may play an important role in providing nutrient source for root colonization of biological control bacteria, and the dctA gene of P. chlororaphis O6 may be an important bacterial trait that is involved in utilization of root exudates.

• Korean Journal of Soil Science and Fertilizer
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• v.43 no.6
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• pp.804-811
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• 2010

The lower portion of sloping paddy fields normally contains excessive moisture and the higher water table caused by the inflow of ground water from the upper part of the field resulting in non-uniform water content distribution. Four drainage methods namely Open Ditch, Vinyl Barrier, Pipe Drainage and Tube Bundle for multiple land use were installed within 1-m position from the lower edge of the upper embankment of sloping alluvial paddy fields. Knowledge of the spatial variability of soil water properties is of primary importance for management of agricultural lands. This study was conducted to evaluate the effect of drainage in the soil on spatial variability of soil water content using the geostatistical analysis. The soil water content was collected by a TDR (Time Domain Reflectometry) sensor after the installation of subsurface drainage on regular square grid of 80 m at 20 m paddy field located at Oesan-ri, Buk-myeon, Changwon-si in alluvial slopping paddy fields ($35^{\circ}22^{\prime}$ N, $128^{\circ}35^{\prime}$). In order to obtain the most accurate field information, the sampling grid was divided 3 m by 3 m unit mesh by four drainage types. The results showed that spatial variance of soil water content by subsurface drainage was reduced, though yield of soybean showed the same trends. Value of "sill" of soil water content with semivariogram was 9.7 in Pipe Drainage, 86.2 in Open Ditch, and 66.8 in Vinyl Barrier and 15.7 in Tube Bundle.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Bio-Environment Control
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• v.24 no.3
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• pp.153-160
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• 2015

The objectives of this study were to determine optimal length of off-time between irrigation cycles to improve irrigation efficiency using a frequency domain reflectometry (FDR) sensor-automated irrigation (FAI) system for tomato (Solanum lycopersicum L.) cultivation aimed at minimizing effluent from coir substrate hydroponics. For treatments, the 5-minute off-time length between 3-minute run-times (defined as 3R5F), 10-minute off-time length between 3-minute run-times (defined as 3R10F), or 15-minute off-time length between 5-minute run-times (defined as 5R15F) were set. During the 3-minute or 5-minute run-time, a 60mL or 80mL of nutrient solution was irrigated to each plant, respectively. Until 62 days after transplant (DAT) during the autumn to winter cultivation, daily irrigation volume was in the order of 3R5F (858mL) > 5R15F (409mL) > 3R10F (306mL) treatment, and daily drainage ratio was in the order of 3R5F (44%) > 5R15F (23%) > 3R10F (14%). Between 63 and 102 DAT, daily irrigated volume was in the order of 5R15F (888mL) > 3R5F (695mL) > 3R10F (524mL) with the highest drainage ratio, 19% (${\pm}2.6$), at the 5R15F treatment. During the spring to summer cultivation, daily irrigation volume and drainage ratio per plant was higher in the 3R5F treatment than that of the 3R10F treatment. For both cultivations, a higher water use efficiency (WUE) was observed under the 3R10F treatment. Integrated all the data suggest that the optimal off-time length is 10 minutes.

• Journal of Korean Society of Archives and Records Management
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• v.1 no.1
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• pp.37-52
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• 2001

1. establishment and development of China archives science: With the centuries-old history of archives and archives management, early China archives science came into being in 1930s, and the research pushed forward by archives enterprise has made great achievements since then. 1.1 Expanding research fields: Foundation

• Journal of Life Science
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• v.17 no.4 s.84
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• pp.462-469
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• 2007

In this study, we isolated, characterized, and compared the vasa homologous genes of diploid and triploid Paragonimus westermani and localized VASA homologous proteins in both lung fluke types. Open reading frames of Pw-vasa-2n and Pw-vasa-3n were of 1812 bp, and encoded deduced proteins of 622 amino acids with calculated molecular weights of 69.0 kDa and 68.9 kDa and pI's of 9.11 and 9.03, respectively. A comparison of these two VASA deduced protein sequences showed that only 6 of the 622 amino acids differed. The deduced sequences of Pw-VASA-2n and Pw-VASA-3n contained eight consensus sequences characteristic of the DEAD-box protein family and their N-terminal regions contained four arginine-glycine-glycine (RGG) motifs. These two lung fluke VASA-like proteins were more similar to those of other VASA proteins than to those of other DEAD-family proteins isolated from several organisms (planarian, zebra fish, mouse, and human). vasa homologous gene transcription and VASA protein expressions in triploid type lung flukes was slightly stronger than in the diploid type. Immunostaining showed that testes and a portion of the ovaries of both diploid and triploid lung flukes reacted strongly to anti-Pw-VASA antibody.

• Journal of Life Science
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• v.19 no.1
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• pp.101-110
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• 2009

The regulation of gene expression plays an important role in cell cycle controls. In this study, a novel $mas2^+$ (mitosis associated protein) gene, a homolog of human SMARCAD1 was isolated and characterized from a fission yeast Schizosaccharomyces pombe (S. pombe) using gene-specific polymerase chain reaction. The isolated gene contained a complete open reading frame capable of encoding 922 amino acid residues with a typical promoter, as judged by nucleotide sequence analysis. It was also found that an SNF2 domain is located, which is involved in the chromosome remodeling. The quantitative analysis of the $mas2^+$ transcript against $adh1^+$ showed that the expression level of $mas2^+$ is high before septum formation in S. pombe. When $mas2^+$ null mutant cells were grown at 27 and $35^{\circ}C$, the cytokinesis of $mas2^+$ null mutant was greatly delayed and a large number of multi-septate and mis-segregated cells were produced. In addition, the number of multi-septate cells significantly increased. When cells were cultured in YES rich medium to increase proliferation, the abnormal phenotypes $mas2^+$ null mutant dramatically increased. These phenotypes could be rescued by an over-expression of the mast gene. The Mas2 protein localized in the nuclei of S. pombe, as evidenced by Mas2-EGFP signals. These results suggest that the $mas2^+$ is homologous to human SMARCAD1 gene and involved in septum formation and chromosome remodeling control.

• Journal of Life Science
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• v.15 no.3 s.70
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• pp.415-422
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• 2005

library of the mutants was prepared by transposon mutagenesis of the Mycobacterium bovis BCG. We screened this library for the resistance to an anti-tuberculosis antibiotic, PA-824. Most of the mutants resistant to the PA-824 were not able to synthesize the coenzyme $F_{420}$ which is normally produced by the wild type M. bovis BCG strains. HPLC analysis of the cellular extract showed that one of those mutants which lost the ability to synthesize $F_{420}$ still produced F0. The insertion site of the transposon in this mutant was determined by an inverse PCR and the transposon was found to be inserted in the Rv2435c open reading frame (ORF). Rv2435c ORF is predicted to encode an 80.3 kDa protein. Rv2435c protein appears to be bound to the cytoplasmic membrane, its N-terminal present in the periplasm and C-terminal in the cytoplasm. The C-terminal portion of this protein is highly homologous with the adenylyl cyclases of both prokaryotes and eukaryotes. There are 15 ORFs which have homology with the class III AC proteins in the genome of the M. tuberculosis and M. bovis. Two of those, Rv1625c and Rv2435c, are highly homologous with the mammalian ACs. We cloned the cytoplasmic domain of the Rv2435c ORF and expressed it with six histidine residues attached on its C-terminal in Escherichia coli to find out if this protein is a genuine AC. Production of that protein in E. coli was proved by purifying the histidine-tagged protein by using the Ni-NTA resin. This protein, however, failed to complement the cya mutation in E. coli, indicating that this protein lacks the AC activity. All of the further attempts to convert this protein to a functional AC by a mutagenesis with UV or hydroxylamine, or construction of several different fusion proteins with Rv1625c failed. It is, therefore, possible that Rv2435c protein might affect the conversion of F0 to $F_{420}$ not by synthesizing cAMP but by some other way.

• Journal of Intelligence and Information Systems
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• v.19 no.1
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• pp.125-139
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• 2013

Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.