In order to investigate the possibility of the recently observed X(5568) being a 0 + tetraquark state, we make an improvement to the study of the related various configuration states in the framework of the QCD sum rules. Particularly, to ensure the quality of the analysis, condensates up to dimension 12 are included to inspect the convergence of operator product expansion (OPE) and improve the final results of the studied states. We note that some condensate contributions could play an important role on the OPE side. By releasing the rigid OPE convergence criterion, we arrive at the numerical value 5.57 +0.35 −0.23 GeV for the scalar-scalar diquark-antidiquark 0 + state, which agrees with the experimental data for the X(5568) and could support its interpretation in terms of a 0 + tetraquark state with the scalar-scalar configuration. The corresponding result for the axial-axial current is calculated to be 5.77 +0.44 −0.33 GeV, which is still consistent with the mass of X(5568) in view of the uncertainty. The feasibility of X(5568) being a tetraquark state with the axial-axial configuration therefore cannot be definitely excluded. For the pseudoscalar-pseudoscalar and the vector-vector cases, their unsatisfactory OPE convergence make it difficult to find reasonable work windows to extract the hadronic information.

An improved search for B 0 s oscillations is performed in the ALEPH data sample collected during the first phase of LEP, and reprocessed in 1998. Three analyses based on complementary event selections are presented. First, decays of B 0 s mesons into hadronic flavour eigenstates are fully reconstructed. This selection yields a small sample of candidates with excellent decay length and momentum resolution and high average B 0 s purity. Semileptonic decays with a reconstructed D − s meson provide a second sample with larger statistics, high average B 0 s purity, but a poorer momentum and decay length resolution due to the partial decay reconstruction. Finally, semileptonic b-hadron decays are inclusively selected and yield the data sample with the highest sensitivity to B 0 s oscillations, as the much higher statistics compensate for the low average B 0 s purity and poorer time resolution. A lower limit is set at ∆m s > 10.9 ps −1 at 95% C.L., significantly lower than the expected limit of 15.2 ps −1 .

Deep reinforcement learning combines the advantages of deep learning and reinforcement learning to make end-to-end perception decisions in complex high-dimensional state action spaces. The paper proposes a deep reinforcement learning method for the deduction of the wargame system, which can better assist the combat commander in wartime decision-making. The simulation proves the effectiveness of the method.

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