• Animal Bioscience
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• v.34 no.5
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• pp.789-800
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• 2021

Objective: Conservation and genetic improvement of cattle breeds require information about genetic diversity and population structure of the cattle. In this study, we investigated the genetic diversity and population structure of the three cattle breeds in the Korean peninsula. Methods: Jeju Black, Hanwoo, Holstein cattle in Korea, together with six foreign breeds were examined. Genetic diversity within the cattle breeds was analyzed with minor allele frequency (MAF), observed and expected heterozygosity (HO and HE), inbreeding coefficient (FIS) and past effective population size. Molecular variance and population structure between the nine breeds were analyzed using a model-based clustering method. Genetic distances between breeds were evaluated with Nei's genetic distance and Weir and Cockerham's FST. Results: Our results revealed that Jeju Black cattle had lowest level of heterozygosity (HE = 0.21) among the studied taurine breeds, and an average MAF of 0.16. The level of inbreeding was -0.076 for Jeju Black, while -0.018 to -0.118 for the other breeds. Principle component analysis and neighbor-joining tree showed a clear separation of Jeju Black cattle from other local (Hanwoo and Japanese cattle) and taurine/indicine cattle breeds in evolutionary process, and a distinct pattern of admixture of Jeju Black cattle having no clustering with other studied populations. The FST value between Jeju Black cattle and Hanwoo was 0.106, which was lowest across the pair of breeds ranging from 0.161 to 0.274, indicating some degree of genetic closeness of Jeju Black cattle with Hanwoo. The past effective population size of Jeju Black cattle was very small, i.e. 38 in 13 generation ago, whereas 209 for Hanwoo. Conclusion: This study indicates genetic uniqueness of Jeju Black cattle. However, a small effective population size of Jeju Black cattle indicates the requirement for an implementation of a sustainable breeding policy to increase the population for genetic improvement and future conservation.

• Korean Journal of Agricultural Science
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• v.48 no.2
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• pp.209-218
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• 2021

This study was carried out to investigate the effects of Bifidobacteria growth promoter BE0623 and a dietary fiber supplement, which included Bifidobacterium lactis BB12, Lactobacillus acidophilus, Streptococcus thermophilus, and Bifidobacterium lactis. In fermented milk containing BE0623, the viable cell count of Bifidobacteria significantly increased by about 45 to 75 times compared to the control, and the titratable acidity increased, whereas the pH decreased. All fractions obtained by isolating BE0623 had Bifidobacteria growth effect. Acacia dietary fiber is a pale yellow powder. It has a viscosity of 60 to 100 cPs and a pH between 4.1 and 5.0. Its general components are less than 10% moisture, more than 90% dietary fiber, and less than 4% ash. The optimal addition ratio of Bifidobacteria growth promoting material was determined to be 0.05%. The general components of the manufactured fermented milk were carbohydrate 17.85%, protein 3.63%, fat 3.00%, and dietary fiber 2.95%. During storage of the fermented milk for 24 days, its titratable acidity, viscosity, and sugar content all met the criteria. In addition, the viable cell counts of Bifidobacteria and lactic acid bacteria in the fermented milk were 1.7 × 10⁸ CFU·mL^-1 and 1.5 × 10⁷ CFU·mL^-1, respectively, and Escherichia coli was negative. There was no significant difference between the control group and the treatment group in the sensory evaluation of sweet, sour, weight, and flavor, and the preference for the treatment group was excellent. The acceptability of the fermented milk of the treated group according to the storage period was excellent in terms of color, flavor, and appearance.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.2
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• pp.97-109
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• 2020

On March 11, 2011, an earthquake followed by a tsunami caused an extended station blackout (SBO) at the Fukushima Dai-ichi NPP Units. The accident was initiated by a total loss of both onsite and offsite electrical power resulting in the loss of the ultimate heat sink for several days, and a consequent core melt in some units where proper mitigation strategies could not be implemented in a timely fashion. To enhance the plant's coping capability, the Diverse and Flexible Strategies (FLEX) were proposed to append the Emergency Operation Procedures (EOPs) by relying on portable equipment as an additional line of defense. To assess the success window of FLEX strategies, all sources of uncertainties need to be considered, using a physics-based model or system code. This necessitates conducting a large number of simulations to reflect all potential variations in initial, boundary, and design conditions as well as thermophysical properties, empirical models, and scenario uncertainties. Alternatively, data-driven models may provide a fast tool to predict the success window of FLEX strategies given the underlying uncertainties. This paper explores the applicability of Artificial Intelligence (AI) to identify the success window of FLEX strategy for extended SBO. The developed model can be trained and validated using data produced by the lumped parameter thermal-hydraulic code, MARS-KS, as best estimate system code loosely coupled with Dakota for uncertainty quantification. A Systems Engineering (SE) approach is used to plan and manage the process of using AI to predict the success window of FLEX strategies under extended SBO conditions.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.94-107
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• 2022

Accidents prevention and mitigation is the highest priority of nuclear power plant (NPP) operation, particularly in the aftermath of the Fukushima Daiichi accident, which has reignited public anxieties and skepticism regarding nuclear energy usage. To deal with accident scenarios more effectively, operators must have ample and precise information about key safety parameters as well as their future trajectories. This work investigates the potential of machine learning in forecasting NPP response in real-time to provide an additional validation method and help reduce human error, especially in accident situations where operators are under a lot of stress. First, a base-case SGTR simulation is carried out by the best-estimate code RELAP5/MOD3.4 to confirm the validity of the model against results reported in the APR1400 Design Control Document (DCD). Then, uncertainty quantification is performed by coupling RELAP5/MOD3.4 and the statistical tool DAKOTA to generate a large enough dataset for the construction and training of neural-based machine learning (ML) models, namely LSTM, GRU, and hybrid CNN-LSTM. Finally, the accuracy and reliability of these models in forecasting system response are tested by their performance on fresh data. To facilitate and oversee the process of developing the ML models, a Systems Engineering (SE) methodology is used to ensure that the work is consistently in line with the originating mission statement and that the findings obtained at each subsequent phase are valid.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3423-3440
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• 2023

In this work, a multivariate time-series machine learning meta-model is developed to predict the transient response of a typical nuclear power plant (NPP) undergoing a steam generator tube rupture (SGTR). The model employs Recurrent Neural Networks (RNNs), including the Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid CNN-LSTM model. To address the uncertainty inherent in such predictions, a Bayesian Neural Network (BNN) was implemented. The models were trained using a database generated by the Best Estimate Plus Uncertainty (BEPU) methodology; coupling the thermal hydraulics code, RELAP5/SCDAP/MOD3.4 to the statistical tool, DAKOTA, to predict the variation in system response under various operational and phenomenological uncertainties. The RNN models successfully captures the underlying characteristics of the data with reasonable accuracy, and the BNN-LSTM approach offers an additional layer of insight into the level of uncertainty associated with the predictions. The results demonstrate that LSTM outperforms GRU, while the hybrid CNN-LSTM model is computationally the most efficient. This study aims to gain a better understanding of the capabilities and limitations of machine learning models in the context of nuclear safety. By expanding the application of ML models to more severe accident scenarios, where operators are under extreme stress and prone to errors, ML models can provide valuable support and act as expert systems to assist in decision-making while minimizing the chances of human error.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.304-311
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• 2022

Construction inspection is a crucial stage that ensures that all contractual requirements of a construction project are verified. The construction inspection capabilities among state highway agencies have been greatly affected due to budget reduction. As a result, efficient inspection practices such as risk-based inspection are required to optimize the use of limited resources without compromising inspection quality. Automated prioritization of textual requirements according to their criticality would be extremely helpful since contractual requirements are typically presented in an unstructured natural language in voluminous text documents. The current study introduces a novel model for predicting the risk level of requirements using machine learning (ML) algorithms. The ML algorithms tested in this study included naïve Bayes, support vector machines, logistic regression, and random forest. The training data includes sequences of requirement texts which were labeled with risk levels (such as very low, low, medium, high, very high) using the fuzzy logic systems. The fuzzy model treats the three risk factors (severity, probability, detectability) as fuzzy input variables, and implements the fuzzy inference rules to determine the labels of requirements. The performance of the model was examined on labeled dataset created by fuzzy inference rules and three different membership functions. The developed requirement risk prediction model yielded a precision, recall, and f-score of 78.18%, 77.75%, and 75.82%, respectively. The proposed model is expected to provide construction inspectors with a means for the automated prioritization of voluminous requirements by their importance, thus help to maximize the effectiveness of inspection activities under resource constraints.

• Microbiology and Biotechnology Letters
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• v.52 no.2
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• pp.179-188
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• 2024

It has been widely documented that stress conditions in aquatic ecosystems could trigger the release of stress hormone (dopamine) in fishes. Such hormone could attract pathogens (such as Aeromonas hydrophila) to initiate its infection in fishes. The major focus of this study was to investigate the effect of the catecholamine derived stress hormone (dopamine) on the motility and hemolytic activity associated with the virulence of A. hydrophila ATCC AH-1N, the causative agent of Motile Aeromonas Septicemia (MAS). The density of bacterial cells used in this study was adjusted at 106 cells/ml. The results showed that dopamine increased swimming motility of A. hydrophila ATCC AH-1N and was proportional to both dopamine hormone concentration and the incubation period. Dopamine concentration of 100 µM in the medium resulted in the highest increment of swimming ability of A. hydrophila ATCC AH-1N. The dopamine hormone was also found to affect the hemolytic activity of A. hydrophila ATCC AH-1N. The optimum hemolytic activity of the pathogen was found at 50 µM dopamine concentration in the medium, and this hemolytic activity was found to decrease when the concentration of dopamine at greater than 50 µM. It can be concluded from this study that dopamine hormone increased the motility and hemolysis capability, as well as the growth rate of A. hydrophila, and hence increased its virulence.

• Horticultural Science & Technology
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• v.19 no.4
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• pp.459-464
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• 2001

Leaf disks from cultivar 'Kennebec' and one selection line (ND 860-2) were cultured on Murashige-Skoog medium with various combinations of indole acetic acid (IAA) and zeatin riboside. Shoots, roots and callus were induced at various combinations of plant growth regulator levels. The medium containing $3.5mg{\cdot}L^{-1}$ IAA and $4.0mg{\cdot}L^{-1}$ zeatin riboside produced the most plantlets. Rooted regenerants were grown in the greenhouse. The growth of regenerated plants obtained from the MS medium supplemented with $7.0mg{\cdot}L^{-1}$ IAA and $3.0mg{\cdot}L^{-1}$ zeatin riboside was significantly greater than those grown from nodal expalnts. In ND 860-2, a leaf chimera with chlorophyll deficient (light yellow) sectors was found in plants regenerated fiom leaf disks (grown on MS medium supplemented with $3.5mg{\cdot}L^{-1}$ IAA and $3.0mg{\cdot}L^{-1}$ zeatin riboside) but not in plants grown from nodal explants. The phenotypic variability was also observed in the tuber number, size and weight.

• Horticultural Science & Technology
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• v.35 no.4
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• pp.431-438
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• 2017

This study investigated the effect of pre-planting nitrogen (N) fertilization levels added to a soilless root medium on the growth of 'Dotaerang Dia' tomato seedlings. The N levels were varied for a total of 7 treatments: 0, 100, 250, 500, 750, 1,000, or $1,500mg{\cdot}L^{-1}$. The pH of the root media gradually rose in all treatments as the seedlings grew; however, the differences in the pH were not significant among the treatments. The electrical conductivity (EC) of the root media was significantly different among the treatments from sowing to week three, then drastically decreased after week four, which diminished the differences in the EC among the treatments. At week six, plant height, leaf length, leaf width, number of leaves, and fresh and dry weights of the shoot were highest for the treatment with $500mg{\cdot}L^{-1}N$. In contrast, the treatment with $1,500mg{\cdot}L^{-1}N$ had the lowest results for all growth measurements. The fresh weight was 67% heavier in the $500mg{\cdot}L^{-1}N$ treatment compared to the $1,500mg{\cdot}L^{-1}N$ treatment. The total N content in the tissues was lowest in the treatment with $0mg{\cdot}L^{-1}N$ and highest in the treatment with $1,000mg{\cdot}L^{-1}N$. The contents of calcium (Ca), magnesium (Mg), and metal micronutrients in the tissues were highest in the $250mg{\cdot}L^{-1}N$ treatment. A previous study demonstrated that adjusting the fertilization level to promote growth to over 90% of the maximum growth is a good strategy for lowering production costs and preventing damage due to excessive fertilizer absorption by crops. Our results indicated that the optimal pre-planting N fertilization level for tomato plug seedlings should be lower than $500mg{\cdot}L^{-1}$ and the optimum tissue N contents should be around 3.21% to 4.60%.