In the existing emergency rescue process for sudden incidents, the lack of effective emergency rescue information communication mechanisms and sharing strategies between mining enterprises and rescue teams often leads to information asymmetry, inefficient information transmission, and low rescue efficiency. To address these issues, this article constructs a payoff matrix for emergency rescue information sharing between mining enterprises and rescue teams based on five premises: bounded rationality, benefit maximization, active participation, cost minimization/loss reduction, and maximization of additional benefits. First, combining Shapley value allocation theory, we analyze the evolutionary trends of the emergency rescue information sharing system. Second, based on the expected returns of both parties choosing to share or not share information, combined with the replicator dynamic equations, we derive the evolutionary stable strategies (ESS) for both parties. We utilize the Jacobian matrix to analyze the stability paths of the system’s equilibrium points. Third, stability tests are conducted on four types of indicators proposed in the evolutionary game model: basic benefits, costs, losses, and additional benefits. In the scenario where both mining enterprises and rescue teams share information, a comprehensive evaluation function is proposed, incorporating the shortest convergence time, the shortest path, and the most stable spectral radius of the Jacobian matrix (the “Triple-Indicator” method) to identify the benchmark preferred initial point for the initial probability of information sharing. Finally, in accordance with the “Mine Rescue Regulations” regarding mandatory comprehensive information sharing, MATLAB simulation is used to verify the rationality and practicality of the optimal strategy. This study provides a typical reference model and strategy for rescue information sharing during emergencies, offering theoretical and practical grounds for revising emergency response communication regulations and constructing safety management information platforms.

Communication is one of the effective means to improve the learning of cooperative policy in multi-agent systems. However, in most real-world scenarios, lossy communication is a prevalent issue. Existing multi-agent reinforcement learning with communication, due to their limited scalability and robustness, struggles to apply to complex and dynamic real-world environments. To address these challenges, we propose a generalized communication-constrained model to uniformly characterize communication conditions across different scenarios. Based on this, we utilize it as a learning prior to distinguish between lossy and lossless messages for specific scenarios. Additionally, we decouple the impact of lossy and lossless messages on distributed decision-making, drawing on a dual mutual information estimatior, and introduce a communication-constrained multi-agent reinforcement learning framework, quantifying the impact of communication messages into the global reward. Finally, we validate the effectiveness of our approach across several communication-constrained benchmarks.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node's infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model by introducing a new epidemic state, that is, the severe state, which characterizes the serious infection of a node different from the initial mild infection. We derive the dynamic equations of our model with the tools of individual-based mean-field approximation and continuous-time Markov chain. We find that, besides infection and recovery rates, the epidemic threshold of our model is determined by the largest real eigenvalue of a communication frequency matrix we construct. Finally, we study how the epidemic spreading is influenced by representative distributions of infection control resources. In particular, we observe that the uniform and Weibull distributions of control resources, which have very close performance, are much better than the Pareto distribution in suppressing the epidemic spreading.

Graphene is a 2-D atomic layer of carbon atoms with unique electronic transport properties such as a high Fermi velocity, an outstanding carrier mobility, and a high carrier saturation velocity, which make graphene an excellent candidate for advanced applications in future electronics. In particular, the potential of graphene in high-speed analog electronics is currently being extensively explored. In this paper, we discuss briefly the basic electronic structure and transport properties of graphene, its large scale synthesis, the role of metal-graphene contact, field-effect transistor (FET) device fabrication (including the issues of gate insulators), and then focus on the electrical characteristics and promise of high-frequency graphene transistors with record-high cutoff frequencies, maximum oscillation frequencies, and voltage gain. Finally, we briefly discuss the first graphene integrated circuits (ICs) in the form of mixers and voltage amplifiers.

By simulating atmospheric deposition experiment, this paper analyzed the relationship between the measured spectral reflectance and the concentrations of contaminants in the snow. It is found that the visible spectrum is sensitive to snow contaminants. From 350nm to 850nm, with the increase concentrations of contaminants in snow, snow reflectivity dramatically decreases. We get the conclusion that the most sensitive bands to snow contaminants are 384nm, 450nm and 1495nm.Using the non-linear regression method to analyze the relationship between spectral reflectance and the contaminants. The results showed the reflectivity of snow at visible bands logarithmically decreases with the snow contaminants increasing; the R2can reach 0.9.To the contrary, the spectral reflectance at nearinfrared increases with the snow contaminants increasing. Therefore, this method can be combined satellite image to forecast the contaminants in the snow at large-scale.