Physical layer security (PLS) provides information-theoretic confidentiality for next-generation wireless systems, yet its practical deployment is fundamentally constrained by the sensitivity and noise characteristics of classical radio receivers. Recent advances in quantum sensing, particularly Rydberg atom-based receivers (RAQRs), present a promising approach to address these limitations. This paper introduces a Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) architecture for secure multi-user uplink communications, where an array of Rydberg vapor cells functions as a high-sensitivity, reconfigurable receiving front-end. We develop a comprehensive end-to-end signal and channel model that incorporates quantum sensor response, wireless fading, and eavesdropper behavior under passive interception. To maximize the achievable secrecy rate, we propose a joint optimization framework that adaptively tunes key system degrees of freedom, including the Rabi frequency of the local oscillator, phase configuration, digital combining matrix, and user transmit power. Extensive simulations validate that RAQ-MIMO substantially outperforms classical MIMO in terms of secrecy rate across varying transmission distances, power budgets, and user loads—particularly under low-signal to noise ratio (SNR) and extended-range regimes. Our work establishes a quantum-sensing-assisted security foundation for future high-assurance wireless networks, bridging atomic physics with scalable communication system design with scalable communication system design under narrowband operation.

The inadequacy of traditional binary interaction networks in characterizing information flow processes within higher-order structures has driven growing research focus toward higher-order networks. Considering reporting mechanism and the dynamics of network scale, this paper proposes a susceptible-infected-quarantine-removed-empty (SIQRE) negative information diffusion model on simplicial complexes. An optimal control strategy, taking into account the system gain, is then implemented. The existence and stability of equilibria, and bi-stability between invasion threshold and persistence threshold are derived. Experiments on synthetic and empirical simplicial complexes reveal the dynamic behavior of the system with discontinuous phase transitions, backward bifurcation and periodic oscillations. An increase in the birth rate makes the system more susceptible to outbreaks of negative information, while the opposite is true for the death rate. Reporting mechanism suppresses discontinuous phase transition. And the synergistic application of preventive and corrective strategies demonstrates superior cost-effectiveness in system control compared to their isolated implementation. Additionally, an identifiability analysis of the model is conducted. Finally, the model parameters are inversely estimated and the diffusion dynamics are predicted using physics-informed neural networks (PINNs) across three instances, and the optimal control is subsequently performed, validating the effectiveness of both the proposed model and the control strategy.

Effective control of information diffusion is quite a significant and universal task in information science and control engineering. The diffusion process and network evolution often share a common time scale. However, previous studies mainly focus on fixed topological structures, ignoring the temporal property of real networks. To fill this gap, we comprehensively investigate the optimal control of information diffusion in temporal networks. First, activity-driven networks (ADNs) are introduced to represent realistic temporal networks. Meanwhile, the node-based susceptible-infected-recovered-susceptible (SIRS) model is employed to describe the diffusion dynamics from a microcosmic perspective. Second, we propose two synergistic optimal control approaches to suppress negative diffusion and enhance positive diffusion, respectively. Then, the optimal control problems of minimizing cumulative system cost and maximizing total system revenue are formulated. Subsequently, the optimal control signals are obtained using the optimal control theory and Pontryagin’s minimum principle. Third, we exploit an implementation framework with a multi-layer hybrid feedback control scheme for large-scale temporal networks. This framework utilizes centralized and distributed techniques to implement different control strategies. Finally, experimental results on various real-world datasets validate the efficiency of the proposed synergistic optimal control approaches and implementation framework.

Identifying information sources plays a significant role in network science and engineering. However, existing source identification approaches generally focus on static networks without considering the temporal features of networks. To this end, we comprehensively study the problem of identifying single and multiple information sources in time-varying networks. Specifically, we first represent the time-varying networks by time aggregated graph (TAG), and employ a microcosmic susceptible-infected-recovered (SIR) model to characterize the diffusion dynamics of each node. Second, in the case of single-source, we exploit a TAG-based reverse infection (RI-TAG) algorithm to specify a set of suspect nodes, which not only reduces the scope of seeking the source but also ensures the feasibility of path calculation. Then, a novel computationally efficient algorithm is proposed to estimate the information source and diffusion time simultaneously. Subsequently, in the case of multi-source, we design a multi-source estimation algorithm, which divides the set of infected nodes into various partitions, and then runs our single-source estimation algorithm in each partition. Moreover, we present an effective algorithm to estimate the number of sources. Finally, experimental results on various synthetic and empirical time-varying networks demonstrate the effectiveness of the proposed algorithms.

The population of open education resource and online self-studying mode have been brought great and profound changes to traditional higher education system. The introduction of Massive Open Online Course (MOOC) not only presents a convenient ways to involved in top university education courses, but also proposes an alternative method to obtain university experience and credit certification. This paper gives a brief but comprehensive review of MOOC application in China including its status quo, opportunities and challenges.