<p>Rumor spreading on social media platforms can significantly impact public opinion and decision-making. In this paper, we proposed an innovative ignorant-spreader-expositor-hibernator-remover (ISEHR) rumor-spreading model with multivariate gatekeepers. Specifically, by analyzing the model's dynamics, we identified the critical threshold that determined the persistence or extinction of rumor spreading. Moreover, we applied the Routh-Hurwitz judgment, Lyapunov theory, and LaSalle's invariance principle to investigate the existence and stability of the rumor-free/rumor equilibrium points. Furthermore, we introduced the optimal control to alleviate rumor spreading with the multivariate gatekeeper mechanism. Finally, extensive numerical simulations validated our theoretical findings, providing insights into the complex dynamics of rumor spreading and the effectiveness of the proposed control measures. Our research contributes to a deeper understanding of rumor spreading on social networks, offering valuable implications for the development of effective strategies to combat rumor.</p>
Audio-driven cross-modal talking head generation has experienced significant advancement in the last several years, and it aims to generate a talking head video that corresponds to a given audio sequence. Out of these approaches, the NeRF-based method can generate videos featuring a specific person with more natural motion compared to the one-shot methods. However, previous approaches failed to distinguish the importance of different regions, resulting in the loss of information-rich region features. To alleviate the problem and improve video quality, we propose MLDF-NeRF, an end-to-end method for talking head generation, which can achieve better vector representation through multi-level feature dynamic fusion. Specifically, we designed two modules in MLDF-NeRF to enhance the cross-modal mapping ability between audio and different facial regions. We initially developed a multi-level tri-plane hash representation that uses three sets of tri-plane hash networks with varying resolutions of limitation to capture the dynamic information of the face more accurately. Then, we introduce the idea of multi-head attention and design an efficient audio-visual fusion module that explicitly fuses audio features with image features from different planes, thereby improving the mapping between audio features and spatial information. Meanwhile, the design helps to minimize interference from facial areas unrelated to audio, thereby improving the overall quality of the representation. The quantitative and qualitative results indicate that our proposed method can effectively generate talk heads with natural actions and realistic details. Compared with previous methods, it performs better in terms of image quality, lip sync, and other aspects.
Information dissemination refers to how information spreads among users on social networks. With the widespread application of mobile communication and internet technologies, people increasingly rely on information on the internet, and the mode of information dissemination is constantly changing. Researchers have performed various studies from mathematical modeling and cascade prediction perspectives to explore the previous problem. However, lacking a comprehensive review of the latest information dissemination models hinders scientific development. As a result, it is essential to review the latest models or methods. In this paper, we review information dissemination models from the past three years and conduct a detailed analysis, such as explanatory and predictive models. Moreover, we provide public datasets, evaluation metrics, and interface tools for researchers focusing more on algorithm design and modeling. Finally, we discuss the model application and future research directions. This paper aims to understand better the research progress and development trends for beginners and guide future research endeavors. We believe this article will attract more researchers’ interest and attention to the information dissemination field on social networks.
In socially aware networking, nodes typically behave selfishly due to resource constraints and social correlations, resulting in low network performance. To incentivize selfish nodes to actively participate in message forwarding, this paper proposes a resource-based dynamic pricing and forced forwarding incentive algorithm (DFIA). Firstly, the algorithm introduces virtual currency as a transaction medium and then designs a pricing function based on factors such as the node’s resource status, participation contribution, location relevance, and social connectivity. It ensures that the forwarding service is transacted at a reasonable price through bargaining rules. Secondly, a forced forwarding strategy is implemented to compel selfish nodes, which are unwilling to participate in other nodes’ message forwarding, to forward a certain number of non-local messages. Meanwhile, in order to prevent nodes from discarding messages and to ensure successful forwarding to the destination, specific rules are used to allocate contribution values to nodes that successfully participate in message forwarding. Lastly, to avoid false quotation behavior, blockchain technology is employed. Transaction information is packaged into blocks and added to the blockchain after consensus validation by other nodes in the network, ensuring the transparency and immutability of transaction data. Simulation results indicate that compared with the existing incentive algorithms, this algorithm not only enhances message delivery probability but also effectively reduces average latency.
Now most residents in village have no medical security.The reform of the cooperation medical treatment in village brings some good result, but the end target of medical security is the integration between city and country.This article elaborated the contents and steps of the integration.It also proposed establishing legal and stable organization to manage the medical security and then carrying on the integration of the medical security between cities and countries on the medical resources and money raising.
Abstract Background The high risk of cardiovascular disease (CVD) being associated with impaired Health-related quality of life (HRQoL). However, few studies have assessed the HRQoL of individuals with a high risk of CVD in Inner Mongolia, or even in China. We aimed to assess health-related quality of life (HRQoL) among individuals in Inner Mongolia with a high risk of CVD and its risk factors, to provide a reference to improve HRQoL in individuals with high CVD risk. Methods From 2015 to 2017, residents of six villages or communities in Inner Mongolia, selected using a multi-stage stratified cluster random sampling method, were invited to complete a questionnaire and undergo physical examination and laboratory testing. We selected participants whose predicted 10-year risk for CVD exceeded 10% as those with high CVD risk. HRQoL in individuals with high CVD risk was investigated based on the EuroQol-5 Dimension (EQ-5D) scale. The Chinese utility value integral system was used to calculate EQ-5D utility scores, and the Tobit regression model were used to analyze the influencing factors of HRQoL among individuals with high CVD risk. Results Of 13,359 participants with high CVD risk, 65.63% reported no problems in any of the five dimensions; the most frequently reported difficulty was pain/discomfort. The mean utility score was 1.000 (0.869, 1.000). Tobit regression analysis showed that sex, age, education level, residence area, household income, physical activity, hypertension, and dyslipidemia were influencing factors of HRQoL. Conclusion We found that female sex, older age, living in an urban area, lower education level, lower household income, and lower physical activity levels were associated with reduced HRQoL. People with a high risk of CVD should maintain their blood glucose and lipid levels within the normal range.
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