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Considering that system analysis codes are used for the evaluation of the performance of Passive Safety Systems (PSSs), it is important to investigate the capability of the system analysis code to reliably predict the heat transfer and natural circulation flow, which are the main phenomena governing the performance of a PSS. Since MARS-KS has been widely validated for heat transfer models, this study focuses on evaluating its capability to predict the single and two-phase pressure drops and natural circulation flow. The straight pipe simulation results indicate that the pressure drop predictions are reliable within ±5 % error margin for the single-phase flow and the errors of pressure drop up to −30 % for the two-phase flow. Through single-phase natural circulation flow analysis, it is concluded that the use of the appropriate K-factor modeling based on the flow regimes is important since the natural circulation flow rate in MARS-KS is mainly affected by the form loss factor modeling. With two-phase natural circulation flow analysis, this study emphasizes the behavior of the system could change significantly depending on the two-phase wall friction and pressure loss modeling. With the analysis results, modeling considerations for the PSS performance evaluation with the system analysis codes are proposed.

The mechanism for the elimination of xenobiotics undergoes three different phases of reactions in organisms. Among these, glutathione S-transferases (GSTs) are classified as phase II detoxification enzymes, catalyzing the conjugation of electrophilic substrates to glutathione or reduced hydroperoxides. This study aimed to investigate the molecular characteristics, detoxification functions, and immune responses of GST omega (LhGSTO1) and kappa (LhGSTK1) in redlip mullet. The open reading frames of LhGSTO1 (720 bp) and LhGSTK1 (687 bp) encoded proteins of 239 and 228 amino acids, respectively. Sequence analysis revealed that LhGSTO1 and LhGSTK1 possessed GSH-binding sites in their N-terminal domains. Substrate-binding sites in the C-terminal domain were exclusively identified in LhGSTO1. In the tissue-specific transcription profile analysis, both LhGSTO1 and LhGSTK1 were ubiquitously expressed in all tissues of healthy mullets. Temporal expression analysis of LhGSTO1 and LhGSTK1 in the blood showed that their expression was significantly modulated by polyinosinic:polycytidylic (poly I:C), lipopolysaccharide (LPS), and Lactococcus garvieae. Different chemical and cellular assays were performed to assess the detoxification and cellular protective abilities of the two proteins. A substrate specificity test using the recombinant proteins revealed that both proteins possessed specific activity toward 1-chloro-2,4-dinitrobenzene (CDNB). In the disk diffusion assay, the smallest clearance zones were observed for LhGSTO1 and LGSTK1 against CdCl2. In the cell protection assay, both LhGSTO1 and LhGSTK1 showed significant Cd detoxification ability compared to the control. Collectively, these results demonstrate that GST omega and kappa are involved in host defense against immune stimulants and xenobiotics in redlip mullet.

Genomic prediction (GP) has emerged an effective tool for addressing the many shortcomings of traditional selective breeding, thereby enhancing the selection process. In this study, we optimized GP methods using 5-fold cross-validation to estimate genome-estimated breeding values for the weight traits of olive flounder (Paralichthys olivaceus). To accomplish our goal, we determined the parentage of the target broodstock and the ability of 11 prediction models to predict the weight traits of 1.8-year-old olive flounders, which were genotyped using a 70 K single nucleotide polymorphism (SNP) array. Moreover, our optimization efforts toward the predictive ability of genomic best linear unbiased prediction (GBLUP), Bayesian B (BB), and random forest (RF) methods encompassed changes in various aspects such as fixed effects, SNP quantity, population size, and phenotypic data collected at different fish ages. Additionally, we assessed the predictive ability for the total length and body depth of fish using GBLUP, BB, and RF. Among the 11 prediction methods used in this study, the BB (0.675), Elastic Net (0.679), and RF (0.698) methods exhibited the highest predictive abilities, whereas the GBLUP (0.637) method demonstrated the lowest. Incorporating information regarding fish sex as a fixed effect substantially improved the predictive ability of GBLUP and BB. For mean models, utilizing 3000–5000 random SNP markers resulted in a higher predictive ability, similar to that obtained using 50,000 SNPs. Increasing the population size reduced the standard deviation of the predictive ability. Notably, phenotypic records from 1.8-year-old fish exhibited a significantly higher predictive ability than those from the other age groups. Furthermore, GBLUP, BB, and RF provided higher predictive abilities for length (0.655–0.852) and body depth (0.665–0.861). These findings may significantly shape future olive flounder genomic selection programs and offer valuable insights into GP in aquaculture.