Search

A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features; and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color features with stroke features, and uses weighted K-means clustering algorithm to extract sample features. The experiment illustrates that the K-CNN hybrid model proposed in the study achieved an accuracy of 94.37% in extracting image information, which is higher than 78.24, 85.69, and 86.78% of C4.5, K-Nearest Neighbor (KNN), and Bi directional Long short-term Memory (BiLSTM) algorithms. In terms of image classification information recognition, the algorithms with better performance from good to poor are: the mixed model > BiLSTM > KNN > C4.5 model, with corresponding accuracy values of 0.938, 0.897, 0.872, and 0.851, respectively. And the number of fluctuation nodes in the mixed model is relatively small. And the sample search time is significantly shorter than other comparison algorithms, with a maximum recognition accuracy of 92.64% for the style, content, color, texture, and direction features of the image, which can effectively recognize the contrast and discrimination of the image. This method effectively provides a new technical means and research direction for digitizing image information.

Covalent organic framework (COF) materials have aroused tremendous interest in photocatalytic applications due to their tunable pore structure and photoelectric properties. The regular nanopore of COF itself presents a strongly confinement effect, which provides a unique regulatory effect for photons, electrons, protons, and other quantum‐scale reaction groups. However, due to the weak surface electron coupling and transfer ability between the reactive groups and basic elements of its structural units, the activity of pure COFs photocatalyst is still not satisfactory. Therefore, the confinement modification strategy of confining low‐dimension entities within COFs has been proposed, thus realizing new active sites construction and band structure regulation has been intensively studied, but yet to be summarized systematically. In this paper, the semi‐conductivity of COFs is discussed dialectically based on photocatalytic thermodynamics, and the influence of internal linkage motifs and stacking behaviors on the band structure is collected. Then, the basic understanding of confinement characteristics and their influence on photocatalytic performance in dynamics is further explained according to the spatial dimension classification of low‐dimension entities. And the application and mechanism of these COF‐based confined catalysts in energy conversion reactions are discussed in detail. Lastly, the current challenges and development prospects of COF‐based confined hetero‐photocatalysts are discussed. [ABSTRACT FROM AUTHOR]

Background: Partial bladder outlet obstruction (pBOO) may lead to bladder remodeling, including fibrosis and extracellular matrix (ECM) deposition. Despite the extensive research on the mechanisms underlying pBOO, potential therapeutic targets for the treatment of pBOO require further research. Dysregulated expression of thrombospondin‐1 (Thbs1) has been reported in various human fibrotic diseases; however, its relationship with pBOO remains unclear. Aims: Investigate the effects of Thbs1 on bladder remodeling caused by pBOO. Methods: We established a pBOO model in Sprague–Dawley rats and performed urodynamic analyses to estimate functional changes in the bladder, validated the histopathological changes in the bladder by using haematoxylin–eosin and Masson's trichrome staining, identified key target genes by integrating RNA sequencing (RNA‐seq) and bioinformatics analyses, validated the expression of related factors using Western blot analysis and RT‐qPCR, and used immunofluorescence staining to probe the potential interaction factors of Thbs1. Results: Urodynamic results showed that pressure‐related parameters were significantly increased in rats with pBOO. Compared with the sham group, the pBOO group demonstrated significant increases in bladder morphology, bladder weight, and collagen deposition. Thbs1 was significantly upregulated in the bladder tissues of rats with pBOO, consistent with the RNA‐seq data. Thbs1 upregulation led to increased expression of matrix metalloproteinase (MMP) 2, MMP9, and fibronectin (Fn) in normal human urinary tract epithelial cells (SV‐HUC‐1), whereas anti‐Thbs1 treatment inhibited the production of these cytokines in TGF‐β1‐treated SV‐HUC‐1. Further experiments indicated that Thbs1 affected bladder remodeling in pBOO via the fibroblast growth factor receptor 3 (FGFR3) pathway. Conclusions: Thbs1 plays a crucial role in bladder remodeling caused by pBOO. Targeting Thbs1 might alleviate ECM damage. Mechanistically, Thbs1 may function via the FGFR signaling pathway by regulating the FGFR3 receptor, identified as the most relevant disease target of pBOO, and FGF2 may be a mediator. These findings suggest that Thbs1 plays a role in BOO development and is a therapeutic target for this condition. [ABSTRACT FROM AUTHOR]