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We study the interaction between heterogeneously charged surfaces in an electrolyte solution by employing classical Density Functional Theory (cDFT) and Monte Carlo simulations. We observe a consistent behavior between cDFT and Monte Carlo simulations regarding force curves and two-dimensional density profiles. Armed with the validated cDFT, we explore the system's behavior under parameters challenging to simulate directly .Our findings include impacts of domain size, domain charge, domain charge configuration, and bulk electrolyte concentration on the osmotic pressure. Remarkably, the force curve is more sensitive to the domain size for asymmetric configuration than symmetry configuration; the bulk concentration weakly influences the force curve independent of the system configurations.

Additional file 6: Figure S7. RT-qPCR analysis of the expression levels of plant hormone pathway related genes. Data represent mean ± standard deviation (SD; n = 3). Significant differences among samples were determined using Student’s t-test (*P

Additional file 3: Figure S4. The prediction of subcellular distributions and proportions of proteins encoded by miRNA target genes corresponding to the degradome transcripts. The value in the outer circle represents the percentage of the number of degradome transcripts of each category, the value in the inner circle represents the number of degradome transcripts of each category.

Additional file 5: Figure S6. Red frame displayed the source data for Fig. 10A.

Additional file 4: Figure S5. Source data for Fig. 9A.

This paper proposes a single image dehazing algorithm using generative adversarial network (GAN) based on feature pyramid network (FPN). This method is an end-to-end image dehazing method, avoiding the physical model dependence. The generator uses MobileNet-V2 as the backbone network, and uses the FPN structure to improve the feature utilization rate of the image, combined with the discriminator formed by the convolutional neural network to form GAN that can improve the training stability and convergence of the generator. The model uses a lightweight MobileNet-V2 network, and the FPN structure also enables multiple-scale feature maps to be obtained while avoiding the use of direct scaling, thus reducing the computational power and memory requirements and allowing the model to operate with limited computational resources. We used the RESIDE training set to train our proposed model and conducted extensive experiments on the test set. The experimental results show that the algorithm has satisfactory results in terms of quality and speed.

Aiming at the demand of intelligent image defogging systems for automatic recognition of fog density, this paper proposes an image fog density recognition method based on multi-feature model and S-DAGSVM. By analyzing the image characteristics of different fog densities, a multi-feature model based on the combination of four features of color, dark channel, information and contrast is constructed to characterize the image fog density, and the features are represented in the form of a histogram. Then, the S-DAGSVM algorithm is proposed to performs supervised learning on the combined feature vectors to realize automatic classification of the image fog density. The experimental results show that the proposed multi-feature model can characterize the fog density more efficiently. Compared with existing multi-class SVM algorithms, the S-DAGSVM algorithm has a higher classification accuracy of up to 96.19%, which has a good reference value for the realization of intelligent image defogging systems.