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Background Plant transcription factors (TFs) are key transcriptional regulators to manipulate the regulatory network of host immunity. However, the globally transcriptional reprogramming of plant TF families in response to pathogens, especially between the resistant and susceptible host plants, remains largely unknown. Results Here, we performed time-series RNA-seq from a resistant pepper line CM334 and a susceptible pepper line EC01 upon challenged with Phytophthora capsici, and enrichment analysis indicated that WRKY family most significantly enriched in both CM334 and EC01. Interestingly, we found that nearly half of the WRKY family members were significantly up-regulated, whereas none of them were down-regulated in the two lines. These induced WRKY genes were greatly overlapped between CM334 and EC01. More strikingly, most of these induced WRKY genes were expressed in time-order patterns, and could be mainly divided into three subgroups: early response (3 h-up), mid response (24 h-up) and mid-late response (ML-up) genes. Moreover, it was found that the responses of these ML-up genes were several hours delayed in EC01. Furthermore, a total of 19 induced WRKY genes were selected for functional identification by virus-induced gene silencing. The result revealed that silencing of CaWRKY03–6, CaWRKY03–7, CaWRKY06–5 or CaWRKY10–4 significantly increase the susceptibility to P. capsici both in CM334 and EC01, indicating that they might contribute to pepper’s basal defense against P. capsici; while silencing of CaWRKY08–4 and CaWRKY01–10 significantly impaired the disease resistance in CM334 but not in EC01, suggesting that these two WRKY genes are prominent modulators specifically in the resistant pepper plants. Conclusions These results considerably extend our understanding of WRKY gene family in pepper’s resistance against P. capsici and provide potential applications for genetic improvement against phytophthora blight.

This paper proposed a hybrid intelligent process model, based on finite-element method (FEM) and Gaussian process regression (GPR), for electrical discharge machining (EDM) process. A model of single-spark EDM process has been constructed based on FEM method, considering the latent heat, variable heat distribution coefficient of cathode (f c ), and plasma flushing efficiency (PFE), to predict material removal rate (MRR) and surface roughness (Ra). This model was validated using reported analytical and experimental results. Then, a GPR model was proposed to establish relationship between input process parameters (pulse current, pulse duration, and discharge voltage) and the process responses (MRR and Ra) for EDM process. The GPR model was trained, tested, and tuned using the data generated from the numerical simulations. Through the GPR model, it was found that responses of EDM process can be accurately predicted for the chosen process conditions. Therefore, for the selection of optimum parameters, the hybrid intelligent model proposed in this paper can be used in EDM process.

In the medium-speed wire electrical discharge machining (MS-WEDM) process, the extremely high temperature and massive electrical discharges in a fraction of 1 s result in the poor surface quality such as high tensile residual stresses, high surface roughness, white layers, and micro cracks. In this paper, an experimental plan for central composite design (CCD) of processing the tool steel (SKD11) has been conducted according to response surface methodology (RSM). The aim is to develop the mathematical model that can correlate the main process parameters of MS-WEDM with machining performance and to seek the optimal parameters on material removal rate (MRR) and 3D surface quality (Sq) by integrated RSM and Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Then, a set of pareto-optimal solutions is obtained. Moreover, from the confirmation experiment, it has been proved that the optimal process-parameter combinations are suitable on the MRR and the 3D surface texture. Eventually, it has also demonstrated that the method of integrated RSM and NSGA-II is an effective way for multi-objective optimization.

The core loss of the grain-oriented silicon steel influences the energy loss of transformer. Pulse energy, point spacing, and scribing spacing are the three key parameters of laser scribing process, which determine the core loss of silicon steel. In this study, the relationship between the three key parameters and core loss reduction was investigated and analyzed. Experiments of laser scribing on 30Q130 silicon steel with different parameter combinations were implemented. The results showed that there was no obvious silicon coating burns in the surface of the steel after laser scribing and that the core loss reduction rate (reduction rate) significantly increased with the increasing laser energy density. However, when the energy density exceeded a certain range, the reduction rate decreased and gradually tended to a stable level. Hence, the parameters had significant influence on the reduction rate of core loss, and the two-factor interaction effects of reduction rate of core loss were prominent when parameters of high pulse energy, small point spacing, and small scribing spacing were selected. The optimization combination values of the three key parameters of laser scribing process on 30Q130 silicon steel was acquired by genetic algorithm–enhanced artificial neural network algorithm. For achieving high reduction of core loss, the optimized combination of pulse energy of 2.52 mJ, small point spacing of 0.22 mm, and small scribing spacing of 3.03 mm were selected, and the reduction rate of 13.12 % was verified through experiment.