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Metamaterials have special magnetic field control capabilities as a matter of concern and have been applied in the field of magnetically-coupled resonant wireless power transfer (MCR-WPT). It is a challenging study topic to seek a metamaterial designed for the target needs of a specific field. The S-parameter retrieval method and equivalent circuit method have received wide attention as the commonly used design methods for metamaterials. The parameters of metamaterials are obtained by repeated simulations, this design process is time-consuming. In this paper, the characteristics of the above two design methods are analyzed and combined. The HFSS and MATLAB software are used to interactively design and optimize the metamaterials used for MCR-WPT characteristic improvement, which effectively simplifies the design process. Furthermore, the square and Koch metamaterial units are designed to optimize the efficiency, and the characteristics of the units are analyzed and compared. Finally, an experimental platform of 6.78 MHz MCR-WPT system is built to analyze the influences of metamaterials on efficiency. The experimental results show that the square metamaterial and Koch metamaterial achieve 28.4% and 24.6% of the maximum power transmission efficiency, respectively, which proves that interactive design can more conveniently design metamaterials and improve the transmission efficiency of the MCR-WPT system.

Taking lithium battery of new energy vehicles as the research object,an echo state network (ESN) model is established to predict the state of charge (SOC) of the vehicle's lithium battery. The cross-validation method is used to optimize the parameters of the ESN to solve difficulty to select arameters of the model. The echo state network is trained by recursive least squares method with forgetting factors to calculate the output weight matrix so as to improve the adaptability and accuracy of the network.The feasibility of the prediction algorithm is further analyzed and verified by the model simulation. The research further analyzes and compares the predicted SOC of the established ESN model, the BP neural network algorithm and radial basis function (RBF) network algorithm under UDDS, US06 and NYCC. The research results show that the established echo state network model is superior to the BP algorithm and RBF algorithm in estimating the performance and effect of lithium-ion battery SOC evaluation. Using ESN model to predict SOC has a good application prospect and can provide a reference for long-term and effective SOC prediction of the lithium battery.

Common rust (CR), caused by Puccina sorghi, is a major foliar disease in maize that leads to quality deterioration and yield losses. To dissect the genetic architecture of CR resistance in maize, this study utilized the susceptible temperate inbred line Ye107 as the male parent crossed with three resistant tropical maize inbred lines (CML312, D39, and Y32) to generate 627 F7 recombinant inbred lines (RILs), with the aim of identifying maize disease-resistant loci and candidate genes for common rust. Phenotypic data showed good segregation between resistance and susceptibility, with varying degrees of resistance observed across different subpopulations. Significant genotype effects and genotype × environment interactions were observed, with heritability ranging from 85.7% to 92.2%. Linkage and genome-wide association analyses across the three environments identified 20 QTLs and 62 significant SNPs. Among these, seven major QTLs explained 66% of the phenotypic variance. Comparison with six SNPs repeatedly identified across different environments revealed overlap between qRUST3-3 and Snp-203,116,453, and Snp-204,202,469. Haplotype analysis indicated two different haplotypes for CR resistance for both the SNPs. Based on LD decay plots, three co-located candidate genes, Zm00001d043536, Zm00001d043566, and Zm00001d043569, were identified within 20 kb upstream and downstream of these two SNPs. Zm00001d043536 regulates hormone regulation, Zm00001d043566 controls stomatal opening and closure, related to trichome, and Zm00001d043569 is associated with plant disease immune responses. Additionally, we performed candidate gene screening for five additional SNPs that were repeatedly detected across different environments, resulting in the identification of five candidate genes. These findings contribute to the development of genetic resources for common rust resistance in maize breeding programs. [ABSTRACT FROM AUTHOR]

A comprehensive study on maize flowering traits, focusing on the regulation of flowering time and the elucidation of molecular mechanisms underlying the genes controlling flowering, holds the potential to significantly enhance our understanding of the associated regulatory gene network. In this study, three tropical maize inbreds, CML384, CML171, and CML444, were used, along with a temperate maize variety, Shen137, as parental lines to cross with Ye107. The resulting F1s underwent seven consecutive generations of self-pollination through the single-seed descent (SSD) method to develop a multiparent population. To investigate the regulation of maize flowering time-related traits and to identify loci and candidate genes, a genome-wide association study (GWAS) was conducted. GWAS analysis identified 556 SNPs and 12 candidate genes that were significantly associated with flowering time-related traits. Additionally, an analysis of the effect of the estimated breeding values of the subpopulations on flowering time was conducted to further validate the findings of the present study. Collectively, this study offers valuable insights into novel candidate genes, contributing to an improved understanding of maize flowering time-related traits. This information holds practical significance for future maize breeding programs aimed at developing high-yielding hybrids. [ABSTRACT FROM AUTHOR]

Summary: Gray leaf spot (GLS) caused by Cercospora zeina or C. zeae‐maydis is a major maize disease throughout the world. Although more than 100 QTLs resistant against GLS have been identified, very few of them have been cloned.Here, we identified a major resistance QTL against GLS, qRglsSB, explaining 58.42% phenotypic variation in SB12×SA101 BC1F1 population. By fine‐mapping, it was narrowed down into a 928 kb region. By using transgenic lines, mutants and complementation lines, it was confirmed that the ZmWAK02 gene, encoding an RD wall‐associated kinase, is the responsible gene in qRglsSB resistant against GLS.The introgression of the ZmWAK02 gene into hybrid lines significantly improves their grain yield in the presence of GLS pressure and does not reduce their grain yield in the absence of GLS.In summary, we cloned a gene, ZmWAK02, conferring large effect of GLS resistance and confirmed its great value in maize breeding. [ABSTRACT FROM AUTHOR]

In this study, hotspot regions, QTL clusters, and candidate genes for eight ear-related traits of maize (ear length, ear diameter, kernel row number, kernel number per row, kernel length, kernel width, kernel thickness, and 100-kernel weight) were summarized and analyzed over the past three decades. This review aims to (1) comprehensively summarize and analyze previous studies on QTLs associated with these eight ear-related traits and identify hotspot bin regions located on maize chromosomes and key candidate genes associated with the ear-related traits and (2) compile major and stable QTLs and QTL clusters from various mapping populations and mapping methods and techniques providing valuable insights for fine mapping, gene cloning, and breeding for high-yield and high-quality maize. Previous research has demonstrated that QTLs for ear-related traits are distributed across all ten chromosomes in maize, and the phenotypic variation explained by a single QTL ranged from 0.40% to 36.76%. In total, 23 QTL hotspot bins for ear-related traits were identified across all ten chromosomes. The most prominent hotspot region is bin 4.08 on chromosome 4 with 15 QTLs related to eight ear-related traits. Additionally, this study identified 48 candidate genes associated with ear-related traits. Out of these, five have been cloned and validated, while twenty-eight candidate genes located in the QTL hotspots were defined by this study. This review offers a deeper understanding of the advancements in QTL mapping and the identification of key candidates associated with eight ear-related traits. These insights will undoubtedly assist maize breeders in formulating strategies to develop higher-yield maize varieties, contributing to global food security. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low multi-step prediction accuracy and poor online prediction adaptability for the algorithm of relevance vector machine,an improved incremental relevance vector machine model is proposed to predict the state-of-charge of Li-ion battery on line. The voltage, charge and discharge current and surface temperature are selected as the model input, and the state-of-charge is used as the output of the model to construct the training set of the model. A fast-sequence sparse Bayesian learning algorithm is chosen for training, and combined with incremental learning method to establish the incremental learning relevance vector machine model for researching on online prediction methods of Li-ion battery. The study found that by adjusting the kernel parameters automatically, it can guarantee a higher prediction accuracy. Experimental results show that this method has the characteristics of high prediction accuracy, fast calculation speed and strong universality, it can provide reference for the prediction and application of the state-of-charge of Li-ion batteries.