Background: A large number of experiments show that the abnormal expression of miRNA is closely related to the occurrence, diagnosis and treatment of diseases. Identifying associations between miRNAs and diseases is important for clinical applications of complex human diseases. However, traditional biological experimental methods and calculation-based methods have many limitations, which lead to the development of more efficient and accurate deep learning methods for predicting miRNA–disease associations. Results: In this paper, we propose a novel model on the basis of adaptive deep propagation graph neural network to predict miRNA–disease associations (ADPMDA). We first construct the miRNA–disease heterogeneous graph based on known miRNA–disease pairs, miRNA integrated similarity information, miRNA sequence information and disease similarity information. Then, we project the features of miRNAs and diseases into a low-dimensional space. After that, attention mechanism is utilized to aggregate the local features of central nodes. In particular, an adaptive deep propagation graph neural network is employed to learn the embedding of nodes, which can adaptively adjust the local and global information of nodes. Finally, the multi-layer perceptron is leveraged to score miRNA–disease pairs. Conclusion: Experiments on human microRNA disease database v3.0 dataset show that ADPMDA achieves the mean AUC value of 94.75% under 5-fold cross-validation. We further conduct case studies on the esophageal neoplasm, lung neoplasms and lymphoma to confirm the effectiveness of our proposed model, and 49, 49, 47 of the top 50 predicted miRNAs associated with these diseases are confirmed, respectively. These results demonstrate the effectiveness and superiority of our model in predicting miRNA–disease associations.

High-quality orchard picking has become a new trend, and achieving the picking of homogeneous fruit is a huge challenge for picking robots. Based on the premise of improving picking efficiency of homo-chromatic fruit in complex environments, this paper proposes a novel homo-chromatic fruit segmentation model under Polar-Net. The model uses Densely Connected Convolutional Networks (DenseNet) as the backbone network, Feature Pyramid Network (FPN) and Cross Feature Network (CFN) to achieve feature extraction and feature discrimination for images of different scales, regions of interest are drawn with the help of Region Proposal Network (RPN), and regression is performed between the features of different layers. In the result prediction part, polar coordinate modeling is performed based on the extracted image features, and the instance segmentation problem is reduced to predict the instance contour for instance center classification and dense distance regression. Experimental results demonstrate that the method effectively improves the segmentation accuracy of homo-chromatic objects and has the characteristics of simplicity and efficiency. The new method has improved the accuracy of segmentation of homo-chromatic objects for picking robots and also provides a reference for segmentation of other fruit and vegetables.

The highly dynamic properties of maneuvering targets make it intractable for radars to predict the target motion states accurately and quickly, and low-grade predicted states depreciate the efficiency of resource allocation. To overcome this problem, we introduce the modified current statistical (MCS) model, which incorporates the input-acceleration transition matrix into the augmented state transition matrix, to predict the motion state of a maneuvering target. Based on this, a robust resource allocation strategy is developed for maneuvering target tracking (MTT) in a netted opportunistic array radar (OAR) system under uncertain conditions. The mechanism of the strategy is to minimize the total transmitting power conditioned on the desired tracking performance. The predicted conditional Cramér–Rao lower bound (PC-CRLB) is deemed as the optimization criterion, which is derived based on the recently received measurement so as to provide a tighter lower bound than the posterior CRLB (PCRLB). For the uncertainty of the target reflectivity, we encapsulate the determined resource allocation model with chance-constraint programming (CCP) to balance resource consumption and tracking performance. A hybrid intelligent optimization algorithm (HIOA), which integrates a stochastic simulation and a genetic algorithm (GA), is employed to solve the CCP problem. Finally, simulations demonstrate the efficiency and robustness of the presented algorithm.