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Recently LHCb declared a new structure $X(6900)$ in the final state di-$J/\psi$ which is popularly regarded as a $cc$-$\bar c\bar c$ tetraquark state. %popularly. Within the Bethe-Salpeter (B-S) frame we study the possible $cc$-$\bar c\bar c$ bound states and the interaction between diquark ($cc$) and antidiquark ($\bar c\bar c$). In this work $cc$ ($\bar c\bar c$) is treated as a color anti-triplet (triplet) axial-vector so the quantum numbers of $cc$-$\bar c\bar c$ bound state are $0^+$, $1^+$ and $2^+$. Learning from the interaction in meson case and using the effective coupling we suggest the interaction kernel for diquark and antidiquark system. Then we deduce the B-S equations for different quantum numbers. Solving these equations numerically we find the spectra of some excited states can be close to the mass of $X(6900)$ when we assign appropriate values for parameter $\kappa$ introduced in the interaction (kernel).We also briefly calculate the spectra of $bb$-$\bar b\bar b$ bound states. Future measurement of $bb$-$\bar b\bar b$ state will help us to determine the exact form of effective interaction., Comment: 14 pages, two figures

The problem of engine unstart of scramjets has not been resolved. In this paper, the mechanism of engine unstart is discussed from the point of view of shock/shock interaction and deflagration-to-detonation transition. The shock/shock interaction leads to the nonlinear, transient and discontinuous process of the supersonic combustion flow field. This process is similar to the deflagration-to-detonation transition process. If the velocity of pre-combustion shock wave is faster than the velocity in the isolator, it will propagate upstream and cause the engine unstart. The C-J detonation velocity is defined as the stable operation boundary of scramjets, which is the maximum shock wave produced by combustion theoretically. The scramjets will work stable if the velocity in the isolator is faster than the corresponding C-J detonation velocity. The combustion characteristics and propulsive performance of scramjets is theoretically analyzed by using C-J detonation theory. For high Mach number scramjets, the velocity in the isolator is much faster than the C-J detonation velocity. Therefore, extra fuel and oxygen can be injected into the combustor to increase the thrust as long as the shock wave velocity driven by the combustion products is slower than the air velocity in the isolator. The theoretical results agree well with the existing experimental results, which can be used as a baseline for the development of scramjets., Comment: submitted to Aerospace Science and Technology,22 pages,8 figures

While considering the spatial and temporal features of traffic, capturing the impacts of various external factors on travel is an essential step towards achieving accurate traffic forecasting. However, existing studies seldom consider external factors or neglect the effect of the complex correlations among external factors on traffic. Intuitively, knowledge graphs can naturally describe these correlations. Since knowledge graphs and traffic networks are essentially heterogeneous networks, it is challenging to integrate the information in both networks. On this background, this study presents a knowledge representation-driven traffic forecasting method based on spatial-temporal graph convolutional networks. We first construct a knowledge graph for traffic forecasting and derive knowledge representations by a knowledge representation learning method named KR-EAR. Then, we propose the Knowledge Fusion Cell (KF-Cell) to combine the knowledge and traffic features as the input of a spatial-temporal graph convolutional backbone network. Experimental results on the real-world dataset show that our strategy enhances the forecasting performances of backbones at various prediction horizons. The ablation and perturbation analysis further verify the effectiveness and robustness of the proposed method. To the best of our knowledge, this is the first study that constructs and utilizes a knowledge graph to facilitate traffic forecasting; it also offers a promising direction to integrate external information and spatial-temporal information for traffic forecasting. The source code is available at https://github.com/lehaifeng/T-GCN/tree/master/KST-GCN., Comment: 11 pages, 14 figures, 7 tables

Agglomerative hierarchical clustering (AHC) is one of the popular clustering approaches. Existing AHC methods, which are based on a distance measure, have one key issue: it has difficulty in identifying adjacent clusters with varied densities, regardless of the cluster extraction methods applied on the resultant dendrogram. In this paper, we identify the root cause of this issue and show that the use of a data-dependent kernel (instead of distance or existing kernel) provides an effective means to address it. We analyse the condition under which existing AHC methods fail to extract clusters effectively; and the reason why the data-dependent kernel is an effective remedy. This leads to a new approach to kernerlise existing hierarchical clustering algorithms such as existing traditional AHC algorithms, HDBSCAN, GDL and PHA. In each of these algorithms, our empirical evaluation shows that a recently introduced Isolation Kernel produces a higher quality or purer dendrogram than distance, Gaussian Kernel and adaptive Gaussian Kernel.