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In this paper, the new solitary wave solutions of the (3+1)-dimensional Boiti-Leon-Manna-Pempinelli equation are obtained by Lie group symmetry method and the extended homoclinic test approach. Firstly, the equation can be reduced to (1+1)-dimensional partial differential equation by Lie group symmetry, and corresponding bilinear forms of the equation are given by symmetry functions. Secondly, the extended homoclinic test approach is employed to obtain the new kink type and singular solitary wave solutions. In addition, some new traveling and non-traveling wave solutions with arbitrary functions and oscillating tail are investigated through the special transformations for the (3+1)-dimensional Boiti-Leon-Manna-Pempinelli equation.

Abstract The noncentrosymmetric ferromagnetic Weyl semimetal CeAlSi with simultaneous space-inversion and time-reversal symmetry breaking provides a unique platform for exploring novel topological states. Here, by employing multiple experimental techniques, we demonstrate that ferromagnetism and pressure can serve as efficient parameters to tune the positions of Weyl nodes in CeAlSi. At ambient pressure, a magnetism-facilitated anomalous Hall/Nernst effect (AHE/ANE) is uncovered. Angle-resolved photoemission spectroscopy (ARPES) measurements demonstrated that the Weyl nodes with opposite chirality are moving away from each other upon entering the ferromagnetic phase. Under pressure, by tracing the pressure evolution of AHE and band structure, we demonstrate that pressure could also serve as a pivotal knob to tune the positions of Weyl nodes. Moreover, multiple pressure-induced phase transitions are also revealed. These findings indicate that CeAlSi provides a unique and tunable platform for exploring exotic topological physics and electron correlations, as well as catering to potential applications, such as spintronics.

Abstract Light engineering of correlated states in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods. Here we demonstrate a light control of correlation gaps in a model charge-density-wave (CDW) and polaron insulator (TaSe4)2I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via the fluence dependence of a longitudinal circular photogalvanic current. This helicity-dependent photocurrent reveals continuous ultrafast phase switches from the polaronic state to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Additional distinctive attributes aligning with the light-induced switches include: the mode-selective coupling of coherent phonons to the polaron and CDW modulation, and the emergence of a non-thermal chiral photocurrent above the pump threshold of CDW-related phonons. The demonstrated ultrafast chirality control of correlated topological states here holds large potentials for realizing axion electrodynamics and advancing quantum-computing applications.

A new-type identification method in the frequency domain by combining a multi-layer neural network and support vector regression is proposed to identify random load of a complex cylindrical shell structure. The kernel function of support vector regression has a great influence on the prediction accuracy of machine learning model, and it is effective to employ the linear function. As the penalty factor is large, the identification accuracy of the Gaussian kernel function is close to the linear kernel function. In the process of random load identification, the prediction accuracy of the neural network using the L-BFGS method is higher than the traditional Adam method. The number of hidden layers of the neural network has little effect on the L-BFGS algorithm, but a great effect on the Adam method. Different levels of noise are introduced to verify the robustness of the machine learning model. Both the support vector regression with linear kernel function and neural network model based on the L-BFGS method have strong robustness. For the noise percentage of 1%, the support vector regression has better prediction accuracy than the neural network, yet the case is contrary for the noise percentage greater than 5%. The verification shows that the neural network model trained by simulation data has better identification accuracy for real load at some frequencies. The load identification method is proposed based on the frequency points which may establish the machine learning model. The mean absolute percentage error shows that the method based on a multi-layer neural network and support vector regression has high identification accuracy. [ABSTRACT FROM AUTHOR]

In this study, we report the synthesis of a new cubic neodymium-rhenium metallic alloy NdRe2 through the utilization of high pressure and laser heating in a diamond anvil cell. NdRe2 crystallizes in the … space group with a lattice parameter equal to 7.486 (2) Å and Z = 8 at 24 (1) GPa and 2,200 (100) K. It was studied using high-pressure single-crystal X-ray diffraction. The compound crystallizes in the cubic MgCu2 structure type. Its successful synthesis further proves that high-pressure high-temperature conditions can be used to obtain alloys holding a Laves phase structure. Ab initio calculations were done to predict the mechanical properties of the material. We also discuss the usage of extreme conditions to synthesize and study materials present in the nuclear waste. [ABSTRACT FROM AUTHOR]

In this study, we report the synthesis of a new cubic neodymium-rhenium metallic alloy NdRe2 through the utilization of high pressure and laser heating in a diamond anvil cell. NdRe2 crystallizes in the … space group with a lattice parameter equal to 7.486 (2) Å and Z = 8 at 24 (1) GPa and 2,200 (100) K. It was studied using high-pressure single-crystal X-ray diffraction. The compound crystallizes in the cubic MgCu2 structure type. Its successful synthesis further proves that high-pressure high-temperature conditions can be used to obtain alloys holding a Laves phase structure. Ab initio calculations were done to predict the mechanical properties of the material. We also discuss the usage of extreme conditions to synthesize and study materials present in the nuclear waste. [ABSTRACT FROM AUTHOR]