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This paper presents a unified framework, called multiRegionFoam, for solving multiphysics problems of the multi-region coupling type within OpenFOAM (FOAM-extend). It is intended to supersede the existing solver with the same name. The design of the new framework is modular, allowing users to assemble a multiphysics problem region-by-region and coupling conditions interface-by-interface. The present approach allows users to choose between deploying either monolithic or partitioned interface coupling for each individual transport equation. The formulation of boundary conditions is generalised in the sense that their implementation is based on the mathematical jump/transmission conditions in the most general form for tensors of any rank. The present contribution focuses on the underlying mathematical model for these types of multiphysics problems, as well as on the software design and resulting code structure that enable a flexible and modular approach. Finally, deployment for different multi-region coupling cases is demonstrated, including conjugate heat, multiphase flows and fuel-cells.

Sensor arrays play a significant role in direction of arrival (DOA) estimation. Specifically, arrays with low redundancy and reduced mutual coupling are desirable. In this paper, we investigate a sensor array configuration that has a restricted sensor spacing and propose a closed-form expression. We also propose several classes of low redundancy (LR) arrays. Interestingly, compared with super nested arrays (SNA) and maximum inter-element spacing constraint (MISC) arrays, one of the proposed arrays has a significant reduction in both redundancy ratio and mutual coupling. Numerical simulations are also conducted to verify the superiority of the proposed array over the known sparse arrays in terms of weight functions, mutual coupling matrices as well and DOA estimation performance.

The traditional Internet has encountered a bottleneck in allocating network resources for emerging technology needs. Network virtualization (NV) technology as a future network architecture, the virtual network embedding (VNE) algorithm it supports shows great potential in solving resource allocation problems. Combined with the efficient machine learning (ML) algorithm, a neural network model close to the substrate network environment is constructed to train the reinforcement learning agent. This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) for the problem that the mapping result of existing heuristic algorithm is easy to converge to the local optimal solution. For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. In view of the problem that the existing VNE algorithm often ignores the underlying resource changes between virtual network requests, a DRL VNE algorithm based on matrix perturbation theory (MPT-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.

Negative Poisson's ratio (NPR) materials have attracted tremendous interest due to their unusual physical properties and potential applications. Certain two-dimensional (2D) monolayer materials have also been found to exhibit NPR and the corresponding deformation mechanism varies. In this study, we found, based on first-principles calculations, that the Poisson's ratio (PR) sign of monolayer Blue Phosphorus Oxide (BPO) can be tuned by strain: the PR is positive under uniaxial strain <= -1% but becomes negative under > 0. The deformation mechanism for BPO under strain depends on the mutual competition between the P-P attraction and P-O repulsion effect, and these two factors induce two different deformation pathways (one with positive PR, and the other with NPR). Moreover, with increasing of strain, both the decreased strength of P-P attraction and the increased strength of P-O repulsion effect modulate the PR of BPO from positive to negative., Comment: 16 Pages, 4 figures

The autocorrelation and the linear complexity of a key stream sequence in a stream cipher are important cryptographic properties. Many sequences with these good properties have interleaved structure, three classes of binary sequences of period $4N$ with optimal autocorrelation values have been constructed by Tang and Gong based on interleaving certain kinds of sequences of period $N$. In this paper, we use the interleaving technique to construct a binary sequence with the optimal autocorrelation of period $2N$, then we calculate its autocorrelation values and its distribution, and give a lower bound of linear complexity. Results show that these sequences have low autocorrelation and the linear complexity satisfies the requirements of cryptography.