• Journal of Life Science
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• v.33 no.8
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• pp.623-631
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• 2023

This study was conducted to develop microsatellite markers in Parapristipoma trilineatum using next-generation sequencing. A total of 402,244,934 reads were generated on the Illumina Hiseq X Ten System, yielding 60,738,985,034 bp of sequences. The de novo assembly resulted in 1,320,995 contigs. A total of 952,326 contigs (0.016%) including 151 microsatellite loci were derived from the 1,320,995 contigs longer than 640 bp. A total of 34 primer sets were designed from the 151 microsatellite loci. As a result, 15 microsatellite loci were chosen and used for assuming population genetic parameters in the wild and farmed populations. The mean number of effective alleles was 12, ranging from 6 to 25. The observed heterozygosity (H_O) and the expected heterozygosity (H_E) ranged between 0.530 and 0.873, with an average of 0.750, and from 0.647 to 0.895, with an average of 0.793, respectively. According to these results, the developed set of 15 microsatellite markers is expected to be useful for the analysis of genetic characteristics in the population of P. trilineatum in Korea. There are requirements now for further genetic information, fishery resource management, breeding guidelines, support with the selection of breeds and studies on the effects of release, all of which will improve species conservation, and through future research, we aim to offer genetic foundational data with that goal.

• Journal of Industrial Convergence
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• v.20 no.12
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• pp.87-96
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• 2022

The Ministry of Land, Infrastructure and Transport has also been promoting the commercialization of land transport technology to commercialize the technologies owned by small and medium-sized venture companies, and to support the transfer and commercialization of public technologies. At this point, in order to improve the investment effect of subsequent new projects and to select excellent research institutes, it is necessary to establish a valid evaluation index system suitable for the purpose of the project. The evaluation index system for subsequent new projects should be linked to the project objectives and goals of the preceding project, and should be selected in consideration of existing evaluation indicators to prevent interruption of research results. Therefore, this thesis sets the evaluation index system into multiple scenarios through hierarchical cluster analysis using the evaluation result data for each evaluation committee for small and medium venture companies participating in the land transportation technology commercialization support project, and then analyzes the structural equation model. As a result of scenario analysis, considering the measurement effect of each path representing the causal relationship between evaluation indicators and the effect of each evaluation index on evaluation items, the scenario with the highest impact on the evaluation result was selected as an improvement plan.

• Journal of the Korean Society for information Management
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• v.40 no.1
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• pp.1-21
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• 2023

This study specifically reviewed the performance of the ranking schemes as an efficient feature selection method for text classification. Until now, feature ranking schemes are mostly based on document frequency, and relatively few cases have used the term frequency. Therefore, the performance of single ranking metrics using term frequency and document frequency individually was examined as a feature selection method for text classification, and then the performance of combination ranking schemes using both was reviewed. Specifically, a classification experiment was conducted in an environment using two data sets (Reuters-21578, 20NG) and five classifiers (SVM, NB, ROC, TRA, RNN), and to secure the reliability of the results, 5-Fold cross-validation and t-test were applied. As a result, as a single ranking scheme, the document frequency-based single ranking metric (chi) showed good performance overall. In addition, it was found that there was no significant difference between the highest-performance single ranking and the combination ranking schemes. Therefore, in an environment where sufficient learning documents can be secured in text classification, it is more efficient to use a single ranking metric (chi) based on document frequency as a feature selection method.

• Journal of the Korean Geotechnical Society
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• v.23 no.12
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• pp.61-73
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• 2007

As a part of Load and Resistance Factor Design(LRFD) code development in Korea, in this paper an intensive reliability analysis was performed to evaluate reliability levels of the two static bearing capacity methods for driven steel pipe piles adopted in Korean Standards for Structure Foundations by the representative reliability methods of First Order Reliability Method(FORM) and Monte Carlo Simulation(MCS). The resistance bias factors for the two static design methods were evaluated by comparing the representative measured bearing capacities with the design values. In determination of the representative bearing capacities of driven steel pipe piles, the 58 data sets of static load tests and soil property tests were collected and analyzed. The static bearing capacity formula and the Meyerhof method using N values were applied to the calculation of the expected design bearing capacity of the piles. The two representative reliability methods(FORM, MCS) based computer programs were developed to facilitate the reliability analysis in this study. Mean Value First Order Second Moment(MVFOSM) approach that provides a simple closed-form solution and two advanced methods of FORM and MCS were used to conduct the intensive reliability analysis using the resistance bias factor statistics obtained, and the results were then compared. In addition, a parametric study was conducted to identify the sensibility and the influence of the random variables on the reliability analysis under consideration.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.339-352
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• 2023

Prediction of the time-related traits of pebble flow inside pebble-bed HTGRs is of great significance for reactor operation and design. In this work, an image-driven approach with the aid of a convolutional neural network (CNN) is proposed to predict the remaining time of initially loaded pebbles and the time interval of paired flow images of the pebble bed. Two types of strategies are put forward: one is adding FC layers to the classic classification CNN models and using regression training, and the other is CNN-based deep expectation (DEX) by regarding the time prediction as a deep classification task followed by softmax expected value refinements. The current dataset is obtained from the discrete element method (DEM) simulations. Results show that the CNN-aided models generally make satisfactory predictions on the remaining time with the determination coefficient larger than 0.99. Among these models, the VGG19+DEX performs the best and its CumScore (proportion of test set with prediction error within 0.5s) can reach 0.939. Besides, the remaining time of additional test sets and new cases can also be well predicted, indicating good generalization ability of the model. In the task of predicting the time interval of image pairs, the VGG19+DEX model has also generated satisfactory results. Particularly, the trained model, with promising generalization ability, has demonstrated great potential in accurately and instantaneously predicting the traits of interest, without the need for additional computational intensive DEM simulations. Nevertheless, the issues of data diversity and model optimization need to be improved to achieve the full potential of the CNN-aided prediction tool.

• Journal of Embryo Transfer
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• v.16 no.3
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• pp.223-231
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• 2001

The success of nuclear transplantation with mammalian oocytes depends critically on the potential of oocytes activation, which mainly caused to prevent the re-accumulation of maturation promoting factor (MPF). This study was conducted to compare the effect of combined treatment of lonomycin with a Hl-histone kinase inhibitor (dimethylaminopurine, DMAP) or cdc2 kinase inhibitor (sodium pyrophosphate, SPP) on activation of bovine oocytes. In vitro matured bovine oocytes with the first polar body (PB) and dense cytoplasm were assigned to 3 experimental groups. For activation treatment, oocytcs were exposed to 5 $\mu$M lonomycin for 5 min (Group 1), and followed by 1.9 mM dimethylaminopurine (DMAP) for 3 h (Group 2) or followed by 2 mM sodium pyrophosphate (SPP) for 3 h (Group 3). The activation effects in the three treatments and the control group (untreated) were judged by the extrusion of the second PB and formation of a pronucleus (PN). Differences among groups were analysed using one-way ANOVA after arc-sine transformation of proportional data. All three treatments led to high activation rates (90% to 95%), with significant difference from the control. However, the extrusion of the second PB and the rate of PN formation differed remarkably among treatments. In Group I and 3, about 95% of the oocytes had extruded the second polar body, but one PN had formed in a higher proportion of oocytes in Group 3 than in Group 1 (90% vs. 5%). In experiment 2, the rates of cleavage and development into blastocysts in Group 1 were significantly lower than those of Group 2 and 3 (8.7% and 0% vs. 50.5% and 11.6%, and 44.6% and 7.2%, respectively, P<0.05). In experiment 3, ~80% of parthenotes in Group 1 were developed with haploid chromosomal sets. However, when ionomycin was followed immediately by DMAP (Group 2). only 20% of parthenotes were haploid. In Group 3, combined treatment with ionomycin and SPP, the appearance of abnormal chromosomal tracts was significantly (P〈0.05) reduced and the proportion of haploid parthenotes was increased to 85% (17/20) than in Group 2. These results demonstrate that SPP acted as a cdc2 kinase inhibitor and formed the haploidy in oocyte activation. Thus, the present study suggests that cdc2 kinase inhibitor, such as sodium pyrophosphate, may have an effective role in oocyte activation for the production of cloned embryos/animals by nuclear transplantation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.367-377
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• 2008

As part of study to develop LRFD (Load and Resistance Factor Design) codes for foundation structures in Korea, resistance factors for static bearing capacity of driven steel pipe piles were calibrated in the framework of reliability theory. The 57 data sets of static load tests and soil property tests conducted in the whole domestic area were collected and these load test piles were sorted into two cases: SPT N at pile tip less than 50, SPT N at pile tip equal to or more than 50. The static bearing capacity formula and the Meyerhof method using N values were applied to calculate the expected design bearing capacities of the piles. The resistance bias factors were evaluated for the two static design methods by comparing the representative measured bearing capacities with the expected design values. Reliability analysis was performed by two types of advanced methods: the First Order Reliability Method (FORM), and the Monte Carlo Simulation (MCS) method using resistance bias factor statistics. The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, in consideration of the reliability level of the current design practice, redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure. Resistance factors of driven steel pipe piles were recommended based on the results derived from the First Order Reliability Method and the Monte Carlo Simulation method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1951-1975
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• 2023

Recent advances in Cognitive Radio Networks (CRN) have elevated them to the status of a critical instrument for overcoming spectrum limits and achieving severe future wireless communication requirements. Collaborative spectrum sensing is presented for efficient channel selection because spectrum sensing is an essential part of CRNs. This study presents an innovative cooperative spectrum sensing (CSS) model that is built on the Firefly Algorithm (FA), as well as machine learning artificial neural networks (ANN). This system makes use of user grouping strategies to improve detection performance dramatically while lowering collaboration costs. Cooperative sensing wasn't used until after cognitive radio users had been correctly identified using energy data samples and an ANN model. Cooperative sensing strategies produce a user base that is either secure, requires less effort, or is faultless. The suggested method's purpose is to choose the best transmission channel. Clustering is utilized by the suggested ANN-FA model to reduce spectrum sensing inaccuracy. The transmission channel that has the highest weight is chosen by employing the method that has been provided for computing channel weight. The proposed ANN-FA model computes channel weight based on three sets of input parameters: PU utilization, CR count, and channel capacity. Using an improved evolutionary algorithm, the key principles of the ANN-FA scheme are optimized to boost the overall efficiency of the CRN channel selection technique. This study proposes the Artificial Neural Network with Firefly Algorithm (ANN-FA) for cognitive radio networks to overcome the obstacles. This proposed work focuses primarily on sensing the optimal secondary user channel and reducing the spectrum handoff delay in wireless networks. Several benchmark functions are utilized We analyze the efficacy of this innovative strategy by evaluating its performance. The performance of ANN-FA is 22.72 percent more robust and effective than that of the other metaheuristic algorithm, according to experimental findings. The proposed ANN-FA model is simulated using the NS2 simulator, The results are evaluated in terms of average interference ratio, spectrum opportunity utilization, three metrics are measured: packet delivery ratio (PDR), end-to-end delay, and end-to-average throughput for a variety of different CRs found in the network.

• Analytical Science and Technology
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• v.19 no.3
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• pp.255-262
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• 2006

In this study, we attempted to evaluate the sorptive loss of VOC upon their contact with different material surfaces including clothing pieces. For the purpose of this study, we developed a method to evaluate such loss by employing a filter holder pack in the thermal desorption line of the GC/FID system. The calibration curves of different aromatic VOCs including benzene, toluene, and xylene were made by loading them at different quantities through the analytical line. A series of experiments were conducted repetitively to draw calibration data sets for all three materials covering glass fiber, cotton, and nylon. The results were then compared in terms of both material types and of VOC types. The extent of sorptive loss increased in a highly systematic manner across different materials such as glass fiber, cotton, and nylon. The patterns of sorptive loss also increased gradually across VOC type such toward in the direction of increasing molecular weights: benzene, toluene, and xylene. According to this experimental study, it is concluded that sorptive behavior of pollutint compounds like VOC can be controlled by the combined effects of both chemical properties and material characteristics.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1C
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• pp.19-29
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• 2008

As a part of study to develop LRFD (Load and Resistance Factor Design) codes for foundation structures in Korea, reliability analyses for driven steel pipe piles are performed and the target reliability indices are selected carefully. The 58 data sets of static load tests and soil property tests conducted in the whole domestic area were collected and analyzed to determine the representative bearing capacities of the piles. The static bearing capacity formula and the Meyerhof method using N values are applied to calculate the expected design bearing capacity of the piles. The resistance bias factors were evaluated for the two static design methods by comparing the representative bearing capacities with the design values. Reliability analysis was performed by two types of advanced methods: First Order Reliability Method (FORM), and Monte Carlo Simulation (MCS) method using resistance bias factor statistics. The static bearing capacity formula exhibited relatively small variation, whereas the Meyerhof method showed relatively high inherent conservatism in the resistance bias factors. Reliability indices for safety factors in the range of 3 to 5 were evaluated respectively as 1.50~2.89 and 1.61~2.72 for both of the static bearing capacity formula and the Meyerhof method. The target reliability indices are selected as 2.0 and 2.33 for group pile case and 2.5 for single pile case, based on the reliability level of the current design practice and considering redundancy of pile group, acceptable risk level, construction quality control, and significance of individual structure.