Ballistic Thermal Rectification in Asymmetric Three-Terminal Mesoscopic Dielectric Systems

By coupling the asymmetric three-terminal mesoscopic dielectric system with a temperature probe, at low temperature, the ballistic heat flux flow through the other two asymmetric terminals in the nonlinear response regime is studied based on the Landauer formulation of transport theory. The thermal rectification is attained at the quantum regime. It is a purely quantum effect and is determined by the dependence of the ratio $\tau_{RC}(\omega)/\tau_{RL}(\omega)$ on $\omega$, the phonon's frequency. Where $\tau_{RC}(\omega)$ and $\tau_{RL}(\omega)$ are respectively the transmission coefficients from two asymmetric terminals to the temperature probe, which are determined by the inelastic scattering of ballistic phonons in the temperature probe. Our results are confirmed by extensive numerical simulations.Comment: 10 pages, 4 figure

Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control

The explosive growth of dynamic and heterogeneous data traffic brings great challenges for 5G and beyond mobile networks. To enhance the network capacity and reliability, we propose a learning-based dynamic time-frequency division duplexing (D-TFDD) scheme that adaptively allocates the uplink and downlink time-frequency resources of base stations (BSs) to meet the asymmetric and heterogeneous traffic demands while alleviating the inter-cell interference. We formulate the problem as a decentralized partially observable Markov decision process (Dec-POMDP) that maximizes the long-term expected sum rate under the users' packet dropping ratio constraints. In order to jointly optimize the global resources in a decentralized manner, we propose a federated reinforcement learning (RL) algorithm named federated Wolpertinger deep deterministic policy gradient (FWDDPG) algorithm. The BSs decide their local time-frequency configurations through RL algorithms and achieve global training via exchanging local RL models with their neighbors under a decentralized federated learning framework. Specifically, to deal with the large-scale discrete action space of each BS, we adopt a DDPG-based algorithm to generate actions in a continuous space, and then utilize Wolpertinger policy to reduce the mapping errors from continuous action space back to discrete action space. Simulation results demonstrate the superiority of our proposed algorithm to benchmark algorithms with respect to system sum rate

Research on vulnerability assessment of coal floor groundwater bursting based on improved local variable weight theory

In order to more scientifically solve the difficult problem of coal seam floor water inrush prediction and evaluation, first of all, a systematic and comprehensive review and comparison of the previous floor water inrush risk evaluation theories and methods, and in-depth and detailed analysis of the most advanced theory based on local variable weight According to the method of evaluating the water inrush vulnerability of the floor, it is considered that the method is not sufficient in the construction of the local state variable weight function, the variable weight interval and the determination of the weight adjustment parameters, and there is room for improvement. For this reason, a new three-interval local state variable weight function is constructed based on the existing theory and knowledge. The constant weight and the correlation coefficient of the constant weight are added to the state variable weight function, and the normalized value accumulation based on the main control factor is given. The new method for determining the threshold of the frequency variable weight interval is adopted, and the method of continuously adjusting and determining the final value of the weighting parameter based on the preliminary given empirical value is adopted according to the evaluation result. After that, a new three-interval variable weight model and its parameter determination method were used to evaluate the water inrush vulnerability of the Ordovician limestone floor in the study area, and the relatively vulnerable area was delineated. Finally, the weight adjustment law of the improved variable weight model is analyzed, and the limiting conditions of the constant weight correlation coefficient are determined through the sensitivity analysis method. It is found that a new three interval variable weight model with constant weight and constant weight correlation coefficient is added It not only reflects the core feature that the weight of the existing variable weight model changes with the change of the state value of the factor and its combination state, but also realizes the positive correlation between the degree of weight adjustment and the constant weight, which illustrates the impact of the variable weight vulnerability evaluation model this time. The improvement is scientific and feasible, and the research results are of great significance for improving the scientificity and practicability of the floor water inrush prediction and evaluation method

Simulating collective behavior in the movement of immigrants by using a spatial prisoner’s dilemma with move option

The movement of immigrants is simulated by using a spatial Prisoner’s Dilemma (PD) with move option. We explore the effect of collective behavior in an evolutionary migrating dynamics. Simulation results show that immigrants adopting collective strategy perform better and thus gain higher survival rate than those not. This research suggests that the clustering of immigrants promotes cooperation