• Korean Journal of Breeding Science
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• v.41 no.3
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• pp.194-204
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• 2009

Cytoplasmic male sterility (CMS) and fertility restoration have been utilized as valuable tools for $F_1$-hybrid seed production in many crops despite laborious breeding processes. Molecular markers for the selection of CMS-related genes help reduce the expenses and breeding times. A previously reported genomic region containing the Ppr-B gene, which is responsible for restoration of fertility and corresponds to the Rfo locus, was used to develop gene-based or so-called "functional" markers for allelic selection of the restorer-of-fertility gene (Rfo) in $F_1$-hybrid breeding of radish (Raphanus sativus L.) Polymorphic sequences among Rfo alleles of diverse breeding lines of radish were examined by sequencing the Ppr-B alleles. However, presence of Ppr-B homolog, designated as Ppr-D, interferes on specific PCR amplification of Ppr-B in certain breeding lines. The organization of Ppr-D, resolved by genome walking, revealed extended homology with Ppr-B even in the promoter region. Interestingly, PCR amplification of Ppr-D was repeatedly unsuccessful in certain breeding lines implying the lack of Ppr-D in these radishes. Ppr-B could only be successfully amplified for analysis through designing primers based on the sequences unique to Ppr-B that exclude interference from Ppr-D gene. Four variants of Rfo alleles were identified from 20 breeding lines. A combination of three molecular markers was developed in order to genotype the Rfo locus based on polymorphisms among four different variants. These markers will be useful in facilitating $F_1$-hybrid cultivar development in radish.

• Journal of The Korean Association For Science Education
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• v.30 no.4
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• pp.429-436
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• 2010

In this study, the difficulties experienced by secondary preservice science teachers in studying the theory of science education for teacher selection test were studied. At first, questions of the internet cafe for preparing the teacher selection test from January 2008 to June 2009 were analyzed in terms of content area. A survey was also administered to 39 preservice science teachers who took the teacher selection test. The rubric used in this study was developed by the Korea Institute for Curriculum and Evaluation and the Korean Association for Science Education. As a result, the most frequent question areas were 'philosophy and history of science' and 'theories and models of teaching and learning science'. In the survey with the preservice science teachers, the most difficult content area was the philosophy of science. The application of teaching and learning model was also one of the most difficult areas. Based on these results, educational implications were discussed.

• Journal of Ecology and Environment
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• v.38 no.1
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• pp.119-122
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• 2015

It is very useful and important to see the status and change of necessary parts in a short period through selecting and observing the bioindicator continually to forecast and prepare the future. Especially, living things are so closely related to the environment that the indicator between the environment and living things shows close interrelationship. Also, the indicator related to environment provides information about representative or decisive environmental phenomenon and is used to simplify complicated facts. Considering wide range of background and application including various indicators such as the change-, destruction-, pollution-, and restoration of habitats, climate change, and species diversity, the closest category includes "environmental indicator," "ecological indicator," and "biodiversity indicator." The selection and use of bioindicator is complicated and difficult. The necessary conditions for the indicator selection are flexible and greatly depend on the goals of investigation such as the indicator for biological diversity investigation of specific area, the indicator for habitat destruction, the indicator for climate change, and the indicator for polluted area. It should meet many various conditions to select a good indicator. In this study, eleven selection standards are established based on domestic and overseas studies on bioindicator selection: species with clear classification and ecology, species distributed in geographically widespread area, species that show clear habitat characteristics, species that can provide early warning for a change, species that are easy and economically benefited for the investigation, species that have many independent individual groups and that is not greatly affected by the size of individual groups, species that is thought to represent the response of other species, species that represent the ecology change caused by the pressure of human influence, species for which researches on climate change have been done, species that is easy to observe, appears for a long time and forms a group with many individuals, and species that are important socially, economically, and culturally.

• Journal of Korean Society of Forest Science
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• v.98 no.2
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• pp.142-147
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• 2009

Hyoscyamus species is sources of the hypnotic and sedative drugs hyoscyamine and scopolamine. Single cells of Hyoscyamus niger were dissociated from suspension cultures and adventitious roots obtained from single-cell clones which were cultured on B5 medium containing 3% (w/v) sucrose, 0.1 mg/L IBA and 0.4% (w/v) gelrite. H. niger adventitious root lines showed wide variation in tropane alkaloids production and growth. An effective selection of 200 root lines was made possible by the application of the 'Dragendorff's reagent' for qualitative detection of the alkaloids from root. A high correlation coefficient (r=0.9390) was observed between the values obtained with the two methods based on HPLC and Dragendorff's reagent analysis. Among the selected roots, the highest scopolamine content was 16.64 mg/g DW (Hn-59), which was 8.82-fold more productive than the lowest alkaloid producing line (Hn-25). Here, we established an efficient selection method on tropane alkaloids production and suggest that the Dragendorff's reagent is of great practical value in selection of invisible compounds.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.101-108
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• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.1828-1847
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• 2014

Coordinated multi-point (CoMP) transmission has been regarded as a potential technology for LTE-Advanced. In frequency division duplexing systems, channel quantization is applied for reporting channel state information (CSI). Considering the dynamic number of cooperation base stations (BSs), asymmetry feature of CoMP channels and high searching complexity, simply increasing the size of the codebook used in traditional multiple antenna systems to quantize the global CSI of CoMP systems directly is infeasible. Per-cell codebook based channel quantization to quantize local CSI for each BS separately is an effective method. In this paper, the theoretical upper bounds of system throughput are derived for two codeword selection schemes, independent codeword selection (ICS) and joint codeword selection (JCS), respectively. The feedback overhead and selection complexity of these two schemes are analyzed. In the simulation, the system throughput of ICS and JCS is compared. Both analysis and simulation results show that JCS has a better tradeoff between system throughput and feedback overhead. The ICS has obvious advantage in complexity, but it needs additional phase information (PI) feedback for obtaining the approximate system throughput with JCS. Under the same number of feedback bits constraint, allocating the number of bits for channel direction information (CDI) and PI quantization can increase the system throughput, but ICS is still inferior to JCS. Based on theoretical analysis and simulation results, some recommendations are given with regard to the application of each scheme respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.5 no.1 s.9
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• pp.27-40
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• 1997

Recently, with the demand for airservice increasing and localization accelerating, airport construction is booming. However, in the case of an unsuitable airport site selection, it might cause a reduction in airport service and a decrease in airport demand. Thus, it is necessary to construct a representative airport and choose a suitable site selection method for economical and efficient airports in order to make the utmost use of airport functions. In this study, GSIS was used to select the airport site and applied to case study areas. GSIS could present a new method for efficient and scientific analysis in airport site selection including various factors over an extensive area. The use of both, the paired comparison method and the delphi method, could improve the objectivity of analysis results in the process considering the relative weight grade of data and priority order of analysis factors, used in airport site selection.

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.1
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• pp.46-53
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• 2001

The purpose of this paper is to develop a new feature selection method for AE signal classification. The neural network of back propagation algorithm is used. The proposed feature selection method uses the difference between feature coordinates in feature space. This method is compared with the existing methods such as Fisher's criterion, class mean scatter criterion and eigenvector analysis in terms of the recognition rate and the convergence speed, using the signals from the defects in welding zone of austenitic stainless steel and in the metal contact of the rotary compressor. The proposed feature selection methods such as 2-D and 3-D criteria showed better results in the recognition rate than the existing ones.

• Food Science and Biotechnology
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• v.17 no.6
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• pp.1221-1227
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• 2008

The application of the cellulase gene (celA) as a selection marker of food-grade integration system was investigated in Lactobacillus (Lb.) casei, Lactococcus lactis, and Leuconostoc (Leu.) mesenteroides. The 6.0-kb vector pOC13 containing celA from Clostridium thermocellum with an integrase gene and a phage attachment site originating from bacteriophage A2 was used for site-specific recombination into chromosomal DNA of lactic acid bacteria (LAB). pOC13 was also equipped with a broad host range plus replication origin from the lactococcal plasmid pWV01, and a controllable promoter of nisA ($P_{nisA}$) for the production of foreign proteins. pOC13 was integrated successfully into Lb. casei EM116, and pOC13 integrants were easily detectable by the formation of halo zone on plates containing cellulose. Recombinant Lb. casei EM 116::pOC13 maintained these traits in the absence of selection pressure during 100 generations. pOC13 was integrated into the chromosome of L. lactis and Leu. mesenteroides, and celA acted as an efficient selection marker. These results show that celA can be used as a food-grade selection marker, and that the new integrative vector could be used for the production of foreign proteins in LAB.

• Information Systems Review
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• v.25 no.1
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• pp.47-73
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• 2023

Management decision-making based on artificial intelligence(AI) plays an important role in helping decision-makers. Business decision-making centered on AI is evaluated as a driving force for corporate growth. AI-based on accurate analysis techniques could support decision-makers in making high-quality decisions. This study proposes an effective decision-making method with the application of multi-label feature selection. In this regard, We present a CFS-BR (Correlation-based Feature Selection based on Binary Relevance approach) that reduces data sets in high-dimensional space. As a result of analyzing sample data and empirical data, CFS-BR can support efficient decision-making by selecting the best combination of meaningful attributes based on the Best-First algorithm. In addition, compared to the previous multi-label feature selection method, CFS-BR is useful for increasing the effectiveness of decision-making, as its accuracy is higher.