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Noting that the gap between individual communication abilities is amplified in the network era, we classify spreaders into super-spreaders and ordinary spreaders by the Pareto principle. In addition, when facing negative information, individuals with higher knowledge levels will recognize it. Based on these, in this study, an improved Ignorants-Hesitant-Super-spreaders-Ordinary spreaders-Wise-Removers (IEASWR) information propagation model was developed. Meanwhile, in view of the gradual saturation, density dependence and nonlinear relationship of the propagation process, the Holling-type II functional response function is introduced to information propagation modeling for the first time making it more realistic. First, the basic reproduction number ℛ 0 is deduced. Then, the global asymptotic stability of the information-free/prevailing equilibria is proved by constructing the Lyapunov function. Furthermore, using the Pontryagin maximum principle, a real-time optimization guidance measure is proposed to achieve the correct guidance of negative information within the expected time. Finally, the validity of the theory is verified through numerical simulation. [ABSTRACT FROM AUTHOR]

The precise identification of drug–protein inter action (DPI) can significantly speed up the drug discovery process. Bioassay methods are time-consuming and expensive to screen for each pair of drug proteins. Machine-learning-based methods cannot accurately predict a large number of DPIs. Compared with traditional computing methods, deep learning methods need less domain knowledge and have strong data learning ability. In this study, we construct a DPI prediction model based on dual channel neural networks with an efficient path attention mechanism, called DCA-DPI. The drug molecular graph and protein sequence are used as the data input of the model, and the residual graph neural network and the residual convolution network are used to learn the feature representation of the drug and protein, respectively, to obtain the feature vector of the drug and the hidden vector of protein. To get a more accurate protein feature vector, the weighted sum of the hidden vector of protein is applied using the neural attention mechanism. In the end, drug and protein vectors are concatenated and input into the full connection layer for classification. In order to evaluate the performance of DCA-DPI, three widely used public data, Human, C.elegans and DUD-E, are used in the experiment. The evaluation metrics values in the experiment are superior to other relevant methods. Experiments show that our model is efficient for DPI prediction. [ABSTRACT FROM AUTHOR]

This paper concerns a class of coupled competitive neural networks, subject to disturbance and discontinuous activation functions. To realize the fixed-time quasi-bipartite synchronization, an aperiodic intermittent controller is initially designed. Subsequently, by combining the fixed-time stability theory and nonsmooth analysis, several criteria are established to ensure the bipartite synchronization in fixed time. Moreover, synchronization error bounds and settling time estimates are provided. Finally, numerical simulations are presented to verify the main results. [ABSTRACT FROM AUTHOR]

To explore the rumor propagation dynamics in online social networks (OSNs) and propose the corresponding control strategies, a novel Ignorants-Spreaders-1-Spreaders-2-Removers (I2SR) rumor-spreading model with general incidence is first formulated in heterogeneous networks. This model covers the bilinear incidence and nonlinear incidence, and considers the time delay in the rumor-spreading process, which is universal and practical. We analytically derive the basic reproduction numbers, which determine the existence of rumor-spreading equilibria but also the global dynamics of the model. Moreover, the Lyapunov–LaSalle principle and the graph-theoretic approach prove the global asymptotic stability of the rumor-free/rumor-spreading equilibria in detail. Significantly, the effects of various strategies, including uniform control, targeted control, and acquaintance control, are implemented and compared. Finally, the developed theoretical results are tested via numerical simulations and a real case data of rumors, showing that the model can more accurately simulate rumor-spreading in OSNs. [ABSTRACT FROM AUTHOR]

In a neutral solution, we investigated the effects of Na2[ZnEDTA] concentrations at 0, 6, 12, 18, and 24 g/L on surface morphology, chemical composition, degradation resistance, and in vitro cytocompatibility of micro-arc oxidation (MAO) coatings developed on WE43 (Mg-Y-Nd-Zr) magnesium alloys. The results show that the enhanced Na2[ZnEDTA] concentration increased the Zn amount but slightly decreased the degradation resistance of MAO-treated coatings. Among the zinc-containing MAO samples, the fabricated sample in the base solution added 6 g/L Na2[ZnEDTA] exhibits the smallest corrosion current density (6.84 × 10−7 A·cm−2), while the sample developed in the solution added 24 g/L Na2[ZnEDTA] and contains the highest Zn content (3.64 wt.%) but exhibits the largest corrosion current density (1.39 × 10−6 A·cm−2). Compared to untreated WE43 magnesium alloys, zinc-containing MAO samples promote initial cell adhesion and spreading and reveal enhanced cell viability. Coating degradation resistance plays a more important role in osseogenic ability than Zn content. Among the untreated WE43 magnesium alloys and the treated MAO samples, the sample developed in the base solution with 6 g/L Na2[ZnEDTA] reveals the highest ALP expression at 14 d. Our results indicate that the MAO samples formed in the solution with Na2[ZnEDTA] promoted degradation resistance and osseogenesis differentiation of the WE43 magnesium alloys, suggesting potential clinic applications. [ABSTRACT FROM AUTHOR]