Restricting the dissemination of negative information on social networks is crucial for maintaining a healthy online environment. Most research emphasizes establishing information dissemination models and designing information processing methods within a single network platform, which is insufficient to address the accelerated information spread resulting from users’ cross-platform dissemination behavior. This article presents a multiplatform information fusion processing method based on the susceptible infected recovered disregarded (SIR-D) model, which reduces the impact of negative information dissemination. First, we introduce an enhanced comprehensive propagation model, termed the SIR-D model, which incorporates a “Disregarded” state to represent users who have received but chose to disregard the recommended content. Additionally, the user state fusion process in the coupled network structure, consisting of ER random network, BA scale-free network and small world network, is described. Moreover, an information processing method is proposed that implements three different measures based on the level of information harm: taking no measures, weakening the effectiveness and blocking the source. Finally, the effectiveness of the information processing method, which incorporates the user state fusion process, is demonstrated through a simulation within complex social networks. The results indicate that the proposed method significantly improves processing efficiency and effectively reduces dangerous information dissemination compared to the nonfusion method.

Human cytomegalovirus (HCMV) is the most common congenital infection. Several HCMV vaccines are in development, but none have yet been approved. An understanding of the kinetics of CMV replication and transmission may inform the rational design of vaccines to prevent this infection. The salivary glands (SG) are an important site of sustained CMV replication following primary infection and during viral reactivation from latency. As such, the strength of the immune response in the SG likely influences viral dissemination within and between hosts. To study the relationship between the immune response and viral replication in the SG, and viral dissemination from the SG to other tissues, mice were infected with low doses of murine CMV (MCMV). Following intra-SG inoculation, we characterized the viral and immunological dynamics in the SG, blood, and spleen, and identified organ-specific immune correlates of protection. Using these data, we constructed compartmental mathematical models of MCMV infection. Model fitting to data and analysis indicate the importance of cellular immune responses in different organs and point to a threshold of infection within the SG necessary for the establishment and spread of infection.

Tensor-based multi-view clustering, which incorporates high-order correlations among views, has emerged as a promising research direction. These methods aim to capture intrinsic structure through a tensor-based constraint and then construct an affinity matrix. However, when constructing the affinity matrix, the negative entries in the coefficient matrices are forced to be positive via absolute operation, which can inadvertently destroy the inherent relationships within the data. Furthermore, existing methods may lack the flexibility to effectively handle and fuse multiple views. To address these issues, we propose a novel approach called Tensorized Scaled Simplex Representation (TSSR) for multi-view clustering. TSSR leverages a low-rank tensor constraint to capture the consensus and complementary information among the views. Besides, it introduces the scaled simplex representation, ensuring non-negative coefficient matrices, thus preserving inherent relationships and enhancing flexibility. Thirdly, TSSR extends the scaling range of the affine constraint to capture authentic structural information. Finally, an auto-weighted strategy assigns ideal weights to diverse views, enabling them to contribute appropriately. We integrate these techniques into a unified framework solved by an iterative algorithm. Experimental results demonstrate that TSSR outperforms state-of-the-art methods in terms of performance and efficiency. The codes and datasets are available athttps://github.com/bingly/TSSR.

Hand, foot, and mouth disease (HFMD) is an infection that is common in children under 5 years old. This disease is not a serious disease commonly, but it is one of the most widespread infectious diseases which can still be fatal. HFMD still poses a threat to the lives and health of children and adolescents. An effective prediction model would be very helpful to HFMD control and prevention. Several methods have been proposed to predict HFMD outpatient cases. These methods tend to utilize the connection between cases and exogenous data, but exogenous data is not always available. In this paper, a novel method combined time series composition and local fusion has been proposed. The Empirical Mode Decomposition (EMD) method is used to decompose HFMD outpatient time series. Linear local predictors are applied to processing input data. The predicted value is generated via fusing the output of local predictors. The evaluation of the proposed model is carried on a real dataset comparing with the state‐of‐the‐art methods. The results show that our model is more accurately compared with other baseline models. Thus, the model we proposed can be an effective method in the HFMD outpatient prediction mission.