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<jats:p>Magnetic two-dimensional (2D) van der Waals (vdWs) materials and their heterostructures attract increasing attentionin the spintronics community due to their various degrees of freedom such as spin, charge, and energy valley, which maystimulate potential applications in the field of low-power and high-speed spintronic devices in the future. This reviewbegins with introducing the long-range magnetic order in 2D vdWs materials and the recent progress of tunning their properties by electrostatic doping and stress. Next, the proximity-effect, current-induced magnetization switching, and the related spintronic devices (such as magnetic tunnel junctions and spin valves) based on magnetic 2D vdWs materials are presented. Finally, the development trend of magnetic 2D vdWs materials is discussed. This review provides comprehensive understandings for the development of novel spintronic applications based on magnetic 2D vdWs materials.</jats:p>

<jats:p>Signal transduction is an important and basic mechanism to cell life activities. The stochastic state transition of receptor induces the release of signaling molecular, which triggers the state transition of other receptors. It constructs a nonlinear sigaling network, and leads to robust switchlike properties which are critical to biological function. Network architectures and state transitions of receptor affect the performance of this biological network. In this work, we perform a study of nonlinear signaling on biological polymorphic network by analyzing network dynamics of the Ca<jats:sup>2+</jats:sup>-induced Ca<jats:sup>2+</jats:sup> release (CICR) mechanism, where fast and slow processes are involved and the receptor has four conformational states. Three types of networks, Erdös–Rényi (ER) network, Watts–Strogatz (WS) network, and BaraBási–Albert (BA) network, are considered with different parameters. The dynamics of the biological networks exhibit different patterns at different time scales. At short time scale, the second open state is essential to reproduce the quasi-bistable regime, which emerges at a critical strength of connection for all three states involved in the fast processes and disappears at another critical point. The pattern at short time scale is not sensitive to the network architecture. At long time scale, only monostable regime is observed, and difference of network architectures affects the results more seriously. Our finding identifies features of nonlinear signaling networks with multistate that may underlie their biological function.</jats:p>

<jats:p>Figures 2(a) and 2(b) in the original paper [<jats:italic>Chin. Phys. B</jats:italic> <jats:bold>30</jats:bold> 066701 (2021)] are replaced by the new ones.</jats:p>

<jats:p>Under the environment of vehicle-to-vehicle (V2V) communication, the traffic information on a large scale can be obtained and used to coordinate the operation of road traffic system. In this paper, a new traffic lattice hydrodynamic model is proposed which considers the influence of multiple-lattice self-anticipative density integration on traffic flow in the V2V environment. Through theoretical analysis, the linear stability condition ofthe new model is derived and the stable condition can be enhanced when more-preceding-lattice self-anticipative density integration effect is taken into account. The property of the unstable traffic density wave in the unstable region is also studied according to thenonlinear analysis. It is shown that the unstable traffic density wave can be described by solving the modified Korteweg–de-Vries (mKdV) equation. Finally, the simulation results demonstrate the validity of the theoretical results. Both theoretical analysis and numerical simulations demonstrate that multiple-lattice self-anticipative density integrationeffect can enhance the stability of traffic flow system in the V2V environment.</jats:p>

<jats:p>Since December 2019, the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade, national policies and the natural environment. To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy, we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations (PDEs), which captures epidemic diffusion along the edges of a network driven by population flow data. In this paper, we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19. Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models. Furthermore, we study the effectiveness of intervention measures, such as traffic lockdowns and social distancing, which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model. To our knowledge, this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.</jats:p>

<jats:p>At present, the global COVID-19 is still severe. More and more countries have experienced second or even third outbreaks. The epidemic is far from over until the vaccine is successfully developed and put on the market on a large scale. Inappropriate epidemic control strategies may bring catastrophic consequences. It is essential to maximize the epidemic restraining and to mitigate economic damage. However, the study on the optimal control strategy concerning both sides is rare, and no optimal model has been built. In this paper, the Susceptible-Infectious-Hospitalized-Recovered (SIHR) compartment model is expanded to simulate the epidemic’s spread concerning isolation rate. An economic model affected by epidemic isolation measures is established. The effective reproduction number and the eigenvalues at the equilibrium point are introduced as the indicators of controllability and stability of the model and verified the effectiveness of the SIHR model. Based on the Deep Q Network (DQN), one of the deep reinforcement learning (RL) methods, the blocking policy is studied to maximize the economic output under the premise of controlling the number of infections in different stages. The epidemic control strategies given by deep RL under different learning strategies are compared for different reward coefficients. The study demonstrates that optimal policies may differ in various countries depending on disease spread and anti-economic risk ability. The results show that the more economical strategy, the less economic loss in the short term, which can save economically fragile countries from economic crises. In the second or third outbreak stage, the earlier the government adopts the control strategy, the smaller the economic loss. We recommend the method of deep RL to specify a policy which can control the epidemic while making quarantine economically viable.</jats:p>

<jats:p>A new algorithm for reconstructing the three-dimensional flow field of the oceanic mesoscale eddies is proposed in this paper, based on variational method. Firstly, with the numerical differentiation Tikhonov regularizer, we reconstruct the continuous horizontal flow field on discrete grid points at each layer in the oceanic region, in terms of the horizontal flow field observations. Secondly, benefitting from the variational optimization analysis and its improvemen, we reconstruct a three-dimensional flow field under the constraint of the horizontal flow and the vertical flow. The results of simulation experiments validate that the relative error of the new algorithm is lower than that of the finite difference method in the case of high grid resolution, which still holds in the case of unknown observational errors or in the absence of vertical velocity boundary conditions. Finally, using the reanalysis horizontal data sourcing from SODA and the proposed algorithm, we reconstruct three-dimensional flow field structure for the real oceanic mesoscale eddy.</jats:p>

<jats:p>In recent years, rumor spreading has caused widespread public panic and affected the whole social harmony and stability. Consequently, how to control the rumor spreading effectively and reduce its negative influence urgently needs people to pay much attention. In this paper, we mainly study the near-optimal control of a stochastic rumor spreading model with Holling II functional response function and imprecise parameters. Firstly, the science knowledge propagation and the refutation mechanism as the control strategies are introduced into a stochastic rumor spreading model. Then, some sufficient and necessary conditions for the near-optimal control of the stochastic rumor spreading model are discussed respectively. Finally, through some numerical simulations, the validity and availability of theoretical analysis is verified. Meanwhile, it shows the significance and effectiveness of the proposed control strategies on controlling rumor spreading, and demonstrates the influence of stochastic disturbance and imprecise parameters on the process of rumor spreading.</jats:p>

<jats:p>We present a three-party reference frame independent quantum key distribution protocol which can be implemented without any alignment of reference frames between the sender and the receiver. The protocol exploits entangled states to establish a secret key among three communicating parties. We derive the asymptotic key rate for the proposed protocol against collective attacks and perform a finite-size key security analysis against general attacks in the presence of statistical fluctuations. We investigate the impact of reference frame misalignment on the stability of our protocol, and we obtain a transmission distance of 180 km, 200 km, and 230 km for rotation of reference frames <jats:italic>β</jats:italic> = <jats:italic>π</jats:italic>/6, <jats:italic>β</jats:italic> = <jats:italic>π</jats:italic>/8 and <jats:italic>β</jats:italic> = 0, respectively. Remarkably, our results demonstrate that our proposed protocol is not heavily affected by an increase in misalignment of reference frames as the achievable transmission distances are still comparable to the case where there is no misalignment in reference frames (when <jats:italic>β</jats:italic> = 0). We also simulate the performance of our protocol for a fixed number of signals. Our results demonstrate that the protocol can achieve an effective key generation rate over a transmission distance of about 120 km with realistic 10<jats:sup>7</jats:sup> finite data signals and approximately achieve 195 km with 10<jats:sup>9</jats:sup> signals. Moreover, our proposed protocol is robust against noise in the quantum channel and achieves a threshold error rate of 22.7%.</jats:p>

<jats:p>In most practical quantum mechanical systems, quantum noise due to decoherence is highly biased towards dephasing. The quantum state suffers from phase flip noise much more seriously than from the bit flip noise. In this work, we construct new families of asymmetric quantum concatenated codes (AQCCs) to deal with such biased quantum noise. Our construction is based on a novel concatenation scheme for constructing AQCCs with large asymmetries, in which classical tensor product codes and concatenated codes are utilized to correct phase flip noise and bit flip noise, respectively. We generalize the original concatenation scheme to a more general case for better correcting degenerate errors. Moreover, we focus on constructing nonbinary AQCCs that are highly degenerate. Compared to previous literatures, AQCCs constructed in this paper show much better parameter performance than existed ones. Furthermore, we design the specific encoding circuit of the AQCCs. It is shown that our codes can be encoded more efficiently than standard quantum codes.</jats:p>