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We study the quantum hair associated with coherent states describing slowly rotating black holes and show how it can be naturally related with the Bekenstein–Hawking entropy and with 1-loop quantum corrections of the metric for the (effectively) non-rotating case. We also estimate corrections induced by such quantum hair to the temperature of the Hawking radiation through the tunnelling method.

Visible-infrared image fusion cannot only reveal respective features of multiband imaging but also combine complementary information. It thus highlights salient information that cannot be directly obtained from a single waveband and enhances scene detection and perception. However, low-light condition for special scenarios, i.e., underground coal mine, impacts the performance of visible-infrared image fusion as they lead to lower contrast for visible light images and loss of local details. In this respect, we propose an infrared and visible image fusion architecture in low-light conditions based on self- and cross-attention (SACA-Fusion). This architecture replaces traditional fusion approaches with a transformer-based fusion network. It better extracts long-range dependencies of images and improves space recovery of fused images. The architecture has an attention mechanism composed of two modules. The self-attention module achieves global interaction and fusion of features and reduces loss in local details; the cross-attention module in nest connect enhances features in low-light conditions and achieves low-contrast space recovery. In the experiment part, through ablation, we confirm that the wonderful fusion strategy is transformer module, rather than RFN or directly connecting. Then, based on comparison experiments on TNO and LLVIP datasets, it is shown that the better fusion performance of the proposed one under some evaluation indicators. Especially in the actual low-light condition, the improvement of the fusion effect is commendable.

In the present work, dielectric and ferroelectric characteristics of Ba 6−p R p Zr 2+p Ta 8−p O 30 ( p  = 1, 2, R = La, Nd, Sm) tungsten bronze ceramics were investigated systematically, and the effects of Zr 4+ and Ta 4+ occupation upon the polar order were emphasized. X-ray diffraction patterns confirmed the tetragonal tungsten bronze structure with P 4/ mbm space group at ambient temperature. Relaxor ferroelectric behavior was determined for all compounds, even for those who had order A 1- and A 2-site occupations, and this was quite different from other tungsten bronze systems. Compared with the Ti- and Nb-based analogue, these Zr- and Ta-based compounds exhibited stronger relaxor nature and obvious polar order disruption. Raman spectra and X-ray photoelectron spectra demonstrated distinct local crystal environment for these compounds with different B -site substitutions. Smaller A -site cation displacement inside the pentagonal channels, frustrating B O 6 deformation, and weaker B –O-bond covalency were the main reasons for the severe disruption of ferroelectric polar order in these filled tungsten bronzes.

An electrode pattern design generating a parabolic voltage distribution, in combination with usage of the linear response range of the liquid crystal (LC) material, has been recently proposed to obtain nearly ideal phase profiles for LC lenses. This technique features low driving voltages, simple structure, compact design, and the absence of high-resistivity (HR) layers. In this work, the universal design principle is discussed in detail, which is applicable not only to LC lens design, but also to other LC devices with any phase profile. Several electrode patterns are presented to form a parabolic voltage distribution. An equivalent electric circuit of the LC lens based on the design principle is developed, and the simulation results are given. In the experiments, an LC lens using the feasible parameters is prepared, and its high-quality performance is demonstrated.

The stability of deeply buried tunnels is significantly influenced by the combined effects of primary joint fissures, blasting-induced damage, high-stress environments, and dynamic disturbances, all of which are key contributors to rock instability. The instability characteristics of rock masses under varying disturbance frequencies and amplitudes remain unclear, making it difficult to establish a reliable basis for tunnel management. This study measured the distribution of joint fissures on the tunnel surface at a burial depth of 1240 m, investigated rock failure characteristics through low-frequency perturbation true triaxial experiments, and analyzed support designs incorporating various combinations of metal mesh, bolts, anchor cables, and shotcrete. The results indicate that as the amplitude and frequency of disturbances increase, the number of cracks in the rock rises significantly and irregularly, while the fractal dimension of the rock’s fracture direction decreases. When the disturbance reaches 10 MPa and 10 Hz, the fractal dimension decreases to a minimum value of 0.62. Additionally, the frequency of pore orientation at angles between 80° and 120° peaks at 52% of its maximum value, approximately 1.68 times that of the original rock. This suggests that the stress experienced by the particles within the rock becomes uneven after disturbance, leading to stress concentration and a pronounced fracture direction. Furthermore, as the amplitude and frequency of disturbances increase, the micropore area observed in scanning electron microscope (SEM) images initially increases rapidly, then continues to grow at a slower rate, with the rate of increase progressively diminishing. Simulations reveal that standard bolts in tunnels subject to dynamic disturbances can effectively resist disturbances with strengths below 40 MPa. However, when the disturbance intensity exceeds 70 MPa, the anchor’s bearing capacity reaches its limit. In the case of bolt-supported tunnels subjected to dynamic disturbances, characteristics such as shallow anchoring depth, low preload force, significant separation of deep surrounding rock, and poor anti-damage ability of the bolts are observed. The use of highly prestressed anchor cable support can resist dynamic disturbances up to 100 MPa and enhance the tunnel’s damage resistance. By combining stress and peak ground acceleration (PGA), the tunnel is classified into five potential risk levels (I to V). Based on this classification, a tunnel support strategy under high-stress disturbances is proposed. Practical applications demonstrate that implementing this strategy reduces the deformation of the surrounding rock by 42.47% to 51.07%, significantly improving the tunnel’s stability.

This paper systematically studies the wave dynamics modeling and numerical simulation of the impact system of hydraulic rock drills. Based on one-dimensional wave theory and multi-physics field coupling methods, a high-fidelity dynamic model was constructed, encompassing hydraulics, mechanics, stress wave propagation, thermodynamics, and damage evolution. Numerical solutions were implemented on the MATLAB/Simulink platform, combined with the spectral element method, implicit Euler method, and adaptive time-step control strategy, effectively addressing the simulation challenges of strongly coupled multi-time-scale systems. Through simulation analysis of the steady-state impact process, key performance indicators such as impact energy, frequency, and energy efficiency were revealed; sensitivity studies were conducted for parameters including oil supply pressure, piston mass, directional valve characteristics, and wave impedance matching; a fatigue life prediction method based on the Lemaitre damage model was established, quantifying the effect of damage evolution on performance degradation. The simulation results were compared with experimental data, showing an error of less than 5%, which verified the reliability of the model. The research results provide a theoretical basis and engineering guidance for structural optimization, test platform design, and reliability improvement of hydraulic rock drills. Furthermore, this paper introduces a three-dimensional wave propagation model, a nonlinear friction model, and a strain-rate-dependent damage criterion, and combines surrogate modeling and multi-objective optimization methods, significantly enhancing the physical completeness and engineering applicability of the model.

A questionnaire survey was conducted with 1153 adolescents to examine how emotional support within family and school contexts relates to adolescents’ prosocial behavior. Results indicated that both family cohesion and teacher emotional support were positively and significantly associated with prosocial behavior. Further analysis revealed that adolescents’ meaning in life mediated these relationships and that self-compassion together with meaning in life served as a sequential mediating pathway. When the direct effects of family cohesion and teacher emotional support on prosocial behavior were compared, teacher emotional support exhibited a significantly stronger direct association. However, no significant differences emerged between the two sources of support concerning the sequential (chain-mediating) pathways. These findings extend current understanding of adolescent prosocial development and highlight the importance of collaborative efforts by families and schools to meet adolescents’ emotional needs and promote prosocial tendencies.

Osteosarcoma is a malignant bone tumor that primarily inflicts the youth. It often metastasizes to the lungs after chemotherapy failure, which eventually shortens patients’ lives. Thus, there is a dire clinical need to develop a novel therapy to tackle osteosarcoma metastasis. Methionine dependence is a special metabolic characteristic of most malignant tumor cells that may offer a target pathway for such therapy. Herein, we demonstrated that methionine deficiency restricted the growth and metastasis of cultured human osteosarcoma cells. A genetically engineered Salmonella , SGN1, capable of overexpressing an L-methioninase and hydrolyzing methionine led to significant reduction of methionine and S-adenosyl-methionine (SAM) specifically in tumor tissues, drastically restricted the growth and metastasis in subcutaneous xenograft, orthotopic, and tail vein-injected metastatic models, and prolonged the survival of the model animals. SGN1 also sharply suppressed the growth of patient-derived organoid and xenograft. Methionine restriction in the osteosarcoma cells initiated severe mitochondrial dysfunction, as evident in the dysregulated gene expression of respiratory chains, increased mitochondrial ROS generation, reduced ATP production, decreased basal and maximum respiration, and damaged mitochondrial membrane potential. Transcriptomic and molecular analysis revealed the reduction of C1orf112 expression as a primary mechanism underlies methionine deprivation-initiated suppression on the growth and metastasis as well as mitochondrial functions. Collectively, our findings unraveled a molecular linkage between methionine restriction, mitochondrial function, and osteosarcoma growth and metastasis. A pharmacological agent, such as SGN1, that can achieve tumor specific deprivation of methionine may represent a promising modality against the metastasis of osteosarcoma and potentially other types of sarcomas as well.

The spontaneous combustion of residual coals in the mined-out area tends to cause an explosion, which is one kind of severe thermodynamic compound disaster of coal mines and leads to serious losses to people's lives and production safety. The prediction and early warning of coal mine thermodynamic disasters are mainly determined by the changes of the index gas concentration pattern in coal mine mined-out areas collected continuously. The time series anomaly pattern detection method is mainly used to reach the state change of gas concentration pattern. The change of gas concentration follows a certain rule as time changes. A great change in the gas concentration indicates the possibility of coal spontaneous combustion and other disasters. To emphasize the features of collected maker gas and overcome the low anomaly detection accuracy caused by the inadequate learning of the normal mode, this paper adopted a method of anomaly detection for time series with difference rate sample entropy and generative adversarial networks. Because the difference rate entropy feature of abnormal data was much larger than that of normal mode, this paper improved the calculation method of the abnormal score by giving different weights to the detection points to enhance the detection rate. To verify the effectiveness of the proposed method, this paper employed simulation models of the mined-out area and adopted coal samples from Dafosi Coal Mine to carry out experiments. Preliminary testing was performed using monitoring data from a coal mine. The experiment compared the entropy results of different time series with the detection results of generative adversarial networks and automatic encoders and showed that the method proposed in this paper had relatively high detection accuracy.