The q-rung orthopair fuzzy sets (q-ROFSs), a novel concept for processing vague information, offer a more potent and all-encompassing method compared to traditional fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets. The inclusion of the parameter q allows for the q-rung orthopair fuzzy sets to capture a broader range of uncertainty of information. In this paper, we present two novel distance measures for q-ROFSs inspired by the Jensen–Shannon divergence, called $D_{JS\_2D}$ and $D_{JS\_3D}$, and we analyze some properties they satisfy, such as non-degeneracy, symmetry, boundedness, and triangular inequality. Then, the normalized distance measures, called $D_{\widetilde{JS\_2D}}$ and $D_{\widetilde{JS\_3D}}$, are proposed and we verify their rationality through numerical experiments. Finally, we apply the proposed distance measures to practical scenarios, including pattern recognition and multicriteria decision-making, and the results demonstrate the effectiveness of the proposed distance measures.

This paper proposes a new network structure, namely the ProNet network. Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment. The baseline model of the ProNet network is UperNet (Unified perceptual parsing Network), and the backbone network is ConvNext (Convolutional Network). A network structure based on depth-separable convolution and 1 × 1 convolution is used, which has good performance and robustness. We further optimise ProNet mainly in two aspects. One is data enhancement using increased noise and slight angle rotation, which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model's performance. Meanwhile, it can effectively expand the training data set, reduce the influence of noise and abnormal data in the data set on the model, and improve the accuracy and reliability of the model. Another is the loss function aspect, and we finally use the focal loss function. The focal loss function is well suited for complex tasks such as object detection. The function will penalise the loss carried by samples that the model misclassifies, thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy. From the experimental results, the evaluation metrics mIoU (mean Intersection over Union) enhanced by 4.47%, and mDice enhanced by 2.92% compared to the baseline network. Better generalization effects and more accurate image segmentation are achieved.

Single-cell sequencing is a technique based on sequencing genomic, transcriptomic and epigenomic analysis at the level of individual cells. Compared with traditional sequencing techniques, it can study intercellular heterogeneity and provide in-depth information on cell distribution, cell types and cell function of a specific population. This paper used blood tissue samples to analyze single-cell sequencing. Cells were downscaled by principal component analysis (PCA) and used Leiden clustering to discover cell heterogeneity in the same cluster and annotated after screening marker genes. Finally, 13 marker genes MTRNR2L8, NKG7, FLNA, IL7R, CCR7, CD8B, MKI67, LGALS3, TYROBP, C0251_TotalSeqC, CD79A, HLA-C, CCL4, and MCM5 in the experimental samples were found by cellular enrichment, which are strongly associated with white stuffy syndrome, skin damage, giant cell virus infection and other diseases are closely linked. In the medical field, the experimental results can be used to study the molecular characteristics and functional differences of different cell types under physiological and pathological conditions, thus providing insight into the pathogenesis and treatment of diseases such as leukoaraiosis syndrome, skin damage, and cytomegalovirus infection.