For a fractional order system (FOS) affected by input noise, the result of general fractional Kalman filter (GFKF) is biased. To overcome this, this brief proposes a new fractional Kalman filter (FKF) algorithm considering input noise. Firstly, it is proved that the result of the GFKF method is biased when the input vector includes the noise. Secondly, we redefine the criterion function of the error of state estimation during the derivation process of the FKF, in which a term about the input noise is added into the covariance matrix during the prior-estimation. Then an improved covariance matrix and Kalman gain are gotten, respectively. Due to the consideration of the input noise, this method can remove the error caused by the input noise. Experiment results illustrate that the algorithm of this brief has superior performance for systems with input noise compared with the GFKF method.

The improvement of the edge detection accuracy and integrity of the single silicon crystal and bright halo is the key to measuring the crystal diameter. This paper proposes an edge detection method with adaptive image inpainting. Firstly, according to the image characteristics, an evaluation function is designed to obtain the optimal order of fractional differential operators for the Canny method. Then, the initial edges of the single silicon crystal and bright halo are obtained with the Canny method based on these operators, and many details of the edges can be obtained. Meanwhile, an improved curvature driven diffusions (CDD) model is proposed for repairing the initial edge, which introduces the gradient information of edge into the CDD model to repair the edge breaks in the initial edges and enhance the edge integrity. Finally, a series of experiments are conducted to validate the effectiveness and performance of the proposed method on the images of practical engineering applications. The experimental results show that the proposed method performs significantly better than the existing methods in the edge detection accuracy and integrity of the single silicon crystal and highlight halo.

This paper proposes a real-time moving objects detecting algorithm based on computer vision. Contour feature is firstly extracted from the initial frame as the feature to describe object, and then the moving object in the latter frame could be tracked and located by marking the location of this feature, at the same time, the feature is dynamically adaptively updated and optimized online. The slight computation burden and robustness to the noise and illumination disturbance make the algorithm have a good real-time quality and reliability. Experimental results indicate that the system reaches the designing requirements.

The pan-cancer initiative aims to study the origin patterns of cancer cell, the processes of carcinogenesis, and the signaling pathways from a perspective that spans across different types of cancer. The construction of the pan-cancer related gene regulatory network is helpful to excavate the commonalities in regulatory relationships among different types of cancers. It also aids in understanding the mechanisms behind cancer occurrence and development, which is of great scientific significance for cancer prevention and treatment. In light of the high dimension and large sample size of pan-cancer omics data, a causal pan-cancer gene regulation network inference algorithm based on stochastic complexity is proposed. With the network construction strategy of local first and then global, the stochastic complexity is used in the conditional independence test and causal direction inference for the candidate adjacent node set of the target nodes. This approach aims to decrease the time complexity and error rate of causal network learning. By applying this algorithm to the sample data of seven types of cancers in the TCGA database, including breast cancer, lung adenocarcinoma, and so on, the pan-cancer related causal regulatory networks are constructed, and their biological significance is verified. The experimental results show that this algorithm can eliminate the redundant regulatory relationships effectively and infer the pan-cancer regulatory network more accurately (https://github.com/LindeEugen/CNI-SC).

Gene regulatory network inference is crucial for revealing the mechanisms of cellular functions and the causal mechanisms of disease occurrence. However, traditional correlation-based methods struggle to identify causal directions, and deep learning methods lack structural interpretability. Moreover, neither of them can efficiently adapt to the search of the solution space for large-scale gene regulatory networks, making it difficult to accurately capture the real regulatory patterns between genes. This study proposes a gene regulatory network inference method based on causal reinforcement learning to address the above problems. Experiments on the DREAM5 dataset and scRNA-seq datasets of six cell types from BEELINE demonstrate that the proposed method outperforms existing mainstream baseline models in inference accuracy, structural sparsity, and biological plausibility. Further validation via GNNExplainer interpretability analysis and KEGG pathway enrichment shows that the constructed gene regulatory network clarifies causal relationships in gene regulation, enhances structural interpretability, and aligns closely with real biological networks and known biological pathways in both topological and functional aspects. The proposed causal reinforcement learning-based framework realizes efficient global optimization of large-scale gene network structures, and the inferred regulatory networks possess high causal rationality and biological interpretability. These results confirm the feasibility and effectiveness of the method for real-world applications in systems biology and computational biology, providing a scalable approach for causal structure modeling of high-dimensional gene expression data.