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The challenge of isotopic identification over a wide angular distribution has limited the measurement of neutron-rich nuclei produced via the multinucleon transfer (MNT) process. To investigate the optimal detection angles for the N=126 isotones, we propose a method to construct the reasonable scattering angles of the MNT products in the dinuclear system (DNS-sysu) model. The reactions $^{136,144}$Xe + $^{208}$Pb are investigated. The calculated results are in rather good agreement with the available experimental data in the reaction $^{136}$Xe + $^{208}$Pb. The entrance channel effects on the scattering angle are investigated. It is found that the scattering angular distribution strongly depends on the isospin and the impact parameter of the collision system. The optimal angle ranges for detecting $N=126$ neutron-rich nuclides $^{204}$Pt, $^{203}$Ir, $^{202}$Os, and $^{201}$Re in the $^{136}$Xe + $^{208}$Pb reaction at the incident energy $E_{c.m.} = 526MeV$ are predicted. Our results suggest that the angle range $45^{\circ} \leqslant \theta_{\mathrm{lab}} \leqslant 50^{\circ}$ is most favorable for detecting unknown N=126 isotones. Given the current difficulties in separating and identifying experimental MNT fragments, the results of this work could provide significant contributions to future experiments., Comment: 6 pages, 5 figures

The accurate determination of the nuclear interaction potential is essential for predicting the fusion cross sections and understanding the reaction mechanism, which plays an important role in the synthesis of superheavy elements. In this letter, a Physics-Informed Neural Network (PINN) method, which combines the neural networks with the calculations of the fusion cross sections via the Hill-Wheeler formula, is developed to optimize the parameters of the Woods-Saxon potential by comparing the experimental values. The correlations between the parameters of Woods-Saxon potential and the reaction partners, which can be quantitatively fitted to a sigmoid-like function with the mass numbers, have been displayed manifestly for the first time. This study could promote the accurate estimation of nucleus-nucleus interaction potential in low energy heavy-ion collisions., Comment: 6 pages, 5 figures

Background: $^{132}$Sn+$^{124}$Sn collisions at the beam energy of 270 MeV$/$nucleon have been performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing impact parameter is one of the important tasks in the experiment as it relates to many observables. Purpose: In this work, we employ three commonly used algorithms in machine learning, the artificial neural network (ANN), the convolutional neural network (CNN) and the light gradient boosting machine (LightGBM), to determine impact parameter by analyzing either the charged particles spectra or several features simulated with events from the ultra-relativistic quantum molecular dynamics (UrQMD) model. Method: To closely imitate experimental data and investigate the generalizability of the trained machine learning algorithms, incompressibility of nuclear equation of state and the in-medium nucleon-nucleon cross sections are varied in the UrQMD model to generate the training data. Results: The mean absolute error $\Delta b$ between the true and the predicted impact parameter is smaller than 0.45 fm if training and testing sets are sampled from the UrQMD model with the same parameter set. However, if training and testing sets are sampled with different parameter sets, $\Delta b$ would increase to 0.8 fm. Conclusion: The generalizability of the trained machine learning algorithms suggests that these machine learning algorithms can be used reliably to reconstruct impact parameter in experiment., Comment: 9 pages,9 figures

In order to solve the suspension overshoot phenomenon existing in the static regulation process of the electric vehicle with electric controlled air suspension, firstly, the relevant factors of air spring are analyzed and the characteristics of the gasbag are simulated and verified in AMESim. Secondly, the model of the electric vehicle body is analyzed according to the law of dynamics and the fuzzy control theory is used to set up the electric vehicle body model in Simulink. In the case of unbalanced load, the effectiveness of the fuzzy controller is used to simulate the phenomenon of “overshoot” in the system.