• Korean Journal of Plant Tissue Culture
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• v.25 no.5
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• pp.329-333
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• 1998

$\beta$-Glucuronidase (GUS) gene of Escherichia coli was introduced into sweet potato (Ipomoea batatas (L.) Lam.) cells by particle bombardment and expressed in the regenerated plants. Microprojectiles coated with DNA of a binary vector pBI121 carrying CaMV35S promoter-GUS gene fusion and a neomycin phosphotransferase gene as selection marker were bombarded on embryogenic calli which originated from shoot apical meristem-derived callus and transferred to Murashige and Skoog (MS) medium supplemented with 1 mg/L 2,4-dichlorophenoxyacetic acid and 100 mg/L kanamycin. Bombarded calli were subcultured at 4 week intervals for six months. Kanamycin-resistant calli transferred to MS medium supplemented with 0.03 mg/L 2iP, 0.03 mg/L ABA, and 50 mg/L kanamycin gave rise to somatic embryos. Upon transfer to MS basal medium without kanamycin, they developed into plantlets. PCR and northern analyses of six regenerants transplanted to potting soil confirmed that the GUS gene was inserted into the genome of the six regenerated plants. A histochemical assay revealed that the GUS gene was preferentially expressed in the vascular bundle and the epidermal layer of leaf, petiole, and tuberous root.

• Nuclear Engineering and Technology
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• v.54 no.5
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• pp.1825-1834
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• 2022

Performing high-fidelity computational fluid dynamics (HF-CFD) to predict the flow and heat transfer state of the coolant in the reactor core is expensive, especially in scenarios that require extensive parameter search, such as uncertainty analysis and design optimization. This work investigated the performance of utilizing a multi-fidelity reduced-order model (MF-ROM) in PWR rod bundles simulation. Firstly, basis vectors and basis vector coefficients of high-fidelity and low-fidelity CFD results are extracted separately by the proper orthogonal decomposition (POD) approach. Secondly, a surrogate model is trained to map the relationship between the extracted coefficients from different fidelity results. In the prediction stage, the coefficients of the low-fidelity data under the new operating conditions are extracted by using the obtained POD basis vectors. Then, the trained surrogate model uses the low-fidelity coefficients to regress the high-fidelity coefficients. The predicted high-fidelity data is reconstructed from the product of extracted basis vectors and the regression coefficients. The effectiveness of the MF-ROM is evaluated on a flow and heat transfer problem in PWR fuel rod bundles. Two data-driven algorithms, the Kriging and artificial neural network (ANN), are trained as surrogate models for the MF-ROM to reconstruct the complex flow and heat transfer field downstream of the mixing vanes. The results show good agreements between the data reconstructed with the trained MF-ROM and the high-fidelity CFD simulation result, while the former only requires to taken the computational burden of low-fidelity simulation. The results also show that the performance of the ANN model is slightly better than the Kriging model when using a high number of POD basis vectors for regression. Moreover, the result presented in this paper demonstrates the suitability of the proposed MF-ROM for high-fidelity fixed value initialization to accelerate complex simulation.