Single nucleotide polymorphism (SNP) arrays are a powerful genotyping tool used in genetic research and genomic breeding programs. Japanese flounder (Paralichthys olivaceus) is an economically-important aquaculture flatfish in many countries. However, the lack of high-efficient genotyping tools has impeded the genomic breeding programs for Japanese flounder. We developed a 50K Japanese flounder SNP array, "Yuxin No. 1," and report its utility in genomic selection (GS) for disease resistance to bacterial pathogens. We screened more than 42.2 million SNPs from the whole-genome resequencing data of 1099 individuals and selected 48 697 SNPs that were evenly distributed across the genome to anchor the array with Affymetrix Axiom genotyping technology. Evaluation of the array performance with 168 fish showed that 74.7% of the loci were successfully genotyped with high call rates (> 98%) and that the polymorphic SNPs had good cluster separations. More than 85% of the SNPs were concordant with SNPs obtained from the whole-genome resequencing data. To validate "Yuxin No. 1" for GS, the arrayed genotyping data of 27 individuals from a candidate population and 931 individuals from a reference population were used to calculate the genomic estimated breeding values (GEBVs) for disease resistance to Edwardsiella tarda. There was a 21.2% relative increase in the accuracy of GEBV using the weighted genomic best linear unbiased prediction (wGBLUP), compared to traditional pedigree-based best linear unbiased prediction (ABLUP), suggesting good performance of the "Yuxin No. 1" SNP array for GS. In summary, we developed the "Yuxin No. 1" 50K SNP array, which provides a useful platform for high-quality genotyping that may be beneficial to the genomic selective breeding of Japanese flounder.
A new generation of fluid pressure forming technology has been developed for the three typical structures of tubes, sheets, and shells, and hard-to-deform material components that are urgently needed for aerospace, aircraft, automobile, and high-speed train industries. In this paper, an overall review is introduced on the state of the art in fundamentals and processes for lower-pressure hydroforming of tubular components, double-sided pressure hydroforming of sheet components, die-less hydroforming of ellipsoidal shells, and dual hardening hot medium forming of hard-to-deform materials. Particular attention is paid to deformation behavior, stress state adjustment, defect prevention, and typical applications. In addition, future development directions of fluid pressure forming technology are discussed, including hyper lower-loading forming for ultra-large non-uniform components, precision forming for intermetallic compound and high-entropy alloy components, intelligent process and equipment, and precise finite element simulation of inhomogeneous and strong anisotropic thin shells.
External short circuit (ESC) of lithium-ion batteries is one of the common and severe electrical failures in electric vehicles. In this study, a novel thermal model is developed to capture the temperature behavior of batteries under ESC conditions. Experiments were systematically performed under different battery initial state of charge and ambient temperatures. Based on the experimental results, we employed an extreme learning machine (ELM)-based thermal (ELMT) model to depict battery temperature behavior under ESC, where a lumped-state thermal model was used to replace the activation function of conventional ELMs. To demonstrate the effectiveness of the proposed model, we compared the ELMT model with a multi-lumped-state thermal (MLT) model parameterized by the genetic algorithm using the experimental data from various sets of battery cells. It is shown that the ELMT model can achieve higher computational efficiency than the MLT model and better fitting and prediction accuracy, where the average root mean squared error (RMSE) of the fitting is 0.65 °C for the ELMT model and 3.95 °C for the MLT model, and the RMES of the prediction under new data set is 3.97 °C for the ELMT model and 6.11 °C for the MLT model.