In this study, layer-by-layer assembly was performed to prepare sodium alginate (SA) layer and walnut-peptide–chitosan (CS) bilayer composite films. Genipin was adopted to crosslink CS and walnut peptide. The properties of walnut peptide-CS-SA composite film were determined, and the influence of material ratio on the performance of composite film was explored. According to the results, the mechanical tensile property, oil absorption property, and water vapor barrier property of the composite film were improved with the presence of genipin. Moreover, the proportion of CS and walnut peptide had significant effects on color, transmittance, mechanical properties, barrier properties, and antioxidant properties of the composite films. Among them, the composite film containing 1% (w/v) CS, 1% (w/v) walnut peptide, and 0.01% (w/v) genipin showed the best performance, with a tensile strength of 3.65 MPa, elongation at break of 30.82%, water vapor permeability of 0.60 g·mm·m−2·h−1·kPa−1, oil absorption of 0.85%, and the three-phase electrochemistry of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging rate of 25.59%. Under this condition, the tensile property, barrier property, and oxidation resistance of the composite film are good, which can provide a good preservation effect for food, and has great application potential.
Traditional Chinese painting is a unique form of artistic expression. Compared with western art painting, it pays more attention to the verve in visual effect, especially ink painting, which makes good use of lines and pays little attention to information such as texture. Some style transfer methods have recently begun to apply traditional Chinese painting style (such as ink wash style) to photorealistic. Ink stylization of different types of real-world photos in a dataset using these style transfer methods has some limitations. When the input images are animal types that have not been seen in the training set, the generated results retain some semantic features of the data in the training set, resulting in distortion. Therefore, in this paper, we attempt to separate the feature representations for styles and contents and propose a style-woven attention network to achieve zero-shot ink wash painting style transfer. Our model learns to disentangle the data representations in an unsupervised fashion and capture the semantic correlations of content and style. In addition, an ink style loss is added to improve the learning ability of the style encoder. In order to verify the ability of ink wash stylization, we augmented the publicly available dataset 𝐶ℎ𝑖𝑝𝑃ℎ𝑖. Extensive experiments based on a wide validation set prove that our method achieves state-of-the-art results. CCS CONCEPTS• Computing methodologies → Computer vision.
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