集宁师范学院

In the article, a quantum Stirling machine is proposed, and its behavior as a quantum heat engine (QHE) or a refrigerator is investigated. The working substance of the machine can be described as a two-qubit XYZ spin chain subjected to a magnetic field and the Dzyaloshinskii-Moriya interaction (DMI). Extensive research has examined the effects of magnetic fields, hot-bath temperature, and DMI on the efficiency and performance coefficient of Stirling heat engines and refrigerators. It has been observed that, by carefully selecting system parameters, the Stirling cycle can be effectively used as a highly efficient QHE or refrigerator. This selection enables optimal performance coefficients, ensuring the system operates at its maximum efficiency. By increasing the temperature of the hot bath and neglecting DMI losses, the system operates as a QHE, achieving a maximum efficiency of 28

Thermo-elastic analysis of a metamaterial reinforced plate subjected to the mechanical and thermal loads is estimated in this paper. The formulation is done using the two-variable sinusoidal shear deformation theory, accounting for out-of-plane normal strain. This study employs an extension of the principle of virtual work into a three-dimensional framework to evaluate the structural response, including deformation, strain, and stress fields of a square composite plate embedded with graphene origami reinforcements under combined thermal and mechanical loads. The plate is manufactured from a Cu reinforced with the origami. The constitutive relations are extended using the material properties derived from the micromechanical models. A higher-order kinematic model with thickness stretch ability was developed for the derivation of the equations. To approve the solution method and derivation procedure, a verification investigation is developed. An enhanced kinematic description, incorporating extensibility and higher-order effects, enables precise analysis of the nanocomposite plate. This capability, combined with the structural efficiency achieved through a reconfigurable, compact geometry, supports potential deployment across sectors such as aerospace, automotive systems, and military technology, where reduced mass and tunable mechanical responses are critical.

Recently, a new class of deformed black-hole exact solutions was constructed in four-dimensional general relativity. The deformation is controlled by a parameter B, which survives after demagnetizing a black hole immersed in an external Bertotti-Robinson magnetic field and changes the global structure of the spacetime into a non-asymptotically flat spindle geometry. Although no astrophysical mechanism for generating such a deformation is currently known, it is natural to ask phenomenologically how large such a geometric deformation could be if it extended over the weak-field solar exterior. Using two classical Solar-System tests, we derive the leading corrections to planetary perihelion precession and to the light travel time in a Shapiro-type configuration. Requiring the B-induced perihelion advance to be smaller than the observational uncertainties in the supplementary perihelion precessions of planets gives the strongest bounds, |B|≲ 10^-24–10^-23 cm^-1, while a Cassini time delay estimate gives a complementary null-geodesic sensitivity at the level |B|≲ 10^-21 cm^-1. These results show that any such spindle deformation, if extended to the solar exterior geometry, must be extremely suppressed on Solar-System scales.

Accurate channel estimation is critical for enabling effective directional beamforming and spectrally efficient transmission in beamspace massive multiple-input multiple-output (MIMO) systems. However, conventional model-driven algorithms are derived from idealized mathematical models and typically suffer severe performance degradation under model mismatches caused by complex and nonideal propagation environments. Although data-driven deep learning (DL) approaches can learn channel characteristics from data, they typically require large-scale training datasets and demonstrate limited generalization capability. To overcome these limitations, we propose a model-data hybrid-driven network (MD-HDN) scheme to address the wideband beamspace channel estimation problem. In the MD-HDN scheme, we unfold the vector approximate message passing (VAMP) algorithm into a trainable network, where a novel shrinkage function is introduced to enhance the estimation accuracy. Extensive numerical results confirm that the proposed MD-HDN scheme can significantly outperform existing schemes under various signal-to-noise ratio (SNR), and achieve substantial improvements in both estimation accuracy and robustness.