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The lining cavities in tunnels have strong concealment and pose significant risks, seriously affecting tunnel operational safety. Therefore, it is necessary to develop efficient and high-precision detection techniques for tunnel lining cavities. In this study, concrete slabs with different parameter cavities were selected as the research object, and experiments on remote detection using Laser Doppler Vibrometry were conducted. During the experiments, the vibration parameters of the concrete surface were measured for cavities of varying sizes and depths, filled with different materials, and under different detection distance conditions. The vibration differences between the defective and healthy parts were analyzed using the spatial spectral entropy algorithm. The results showed that for cavities with side lengths of 200mm, depths of 50mm, and filled with hollow wooden boxes, the maximum velocity amplitudes of the surface concrete were 10.68, 3.55, and 4.01 times higher than those of the healthy parts, respectively. Moreover, at the same frequency, larger cavity areas and shallower depths resulted in greater surface vibration amplitudes. The vibration amplitudes of the surface with hollow wooden box filling were higher than those with foam polystyrene board filling. With increasing detection distance, the overall surface vibration velocity of the cavities was higher at a distance of 3 m from the laser probe compared to 5 m, indicating the ability to quantitatively describe the apparent vibration characteristics of concrete cavities under different parameters. This study demonstrates the significant effectiveness of laser Doppler vibrometry in remote detection of lining cavities in tunnels. [ABSTRACT FROM AUTHOR]

In order to reduce vibration and noise with wide frequency, a new extended arrow tetragonal lattice topology is proposed. The band gap characteristics are discussed by combining the finite element method and Bloch's theorem. The generation principle of band gap is explained by vibration mode analysis. By topological optimization of the structure, band gap has obvious optimization effect. The propagation characteristics of elastic waves with a specific frequency in the structure are studied from the point of view of energy. Finally, the band gap frequency range is compared with the transmission function, and the stress cloud diagram is analyzed. The research shows that the structure has good band gaps and can play a good role in vibration and noise reduction. Through the topological design of the structure, the band gap frequency range of the structure is optimized, which provides a new design idea for wide frequency vibration attenuation. [ABSTRACT FROM AUTHOR]

The temperature gradient plays a critical role in the evolution of pore structure and mechanical properties of rock, but the underlying mechanisms remain unclear. This study conducts experiments on granite to investigate the effects of temperature gradients on pore structure and rupture propagation under different media, specifically liquid nitrogen and air. The pore structures and granite's transverse relaxation time (T2) are quantified using nuclear magnetic resonance (NMR). Then, Brazilian disk experiments are performed to explore the mechanical properties of granite following heating-holding treatments. Under the same temperature gradient, cooling with liquid nitrogen significantly influences the pore structures and mechanical properties of granite compared to air cooling. As the heat treatment temperature gradient increases, the mass loss rate, volume expansion ratio, and NMR porosity of granite increase, while the density, longitudinal wave velocity, and peak stress decrease. Splitting failure is caused by the constant propagation of the prominent cracks generated from the loading end to the load-bearing end. With increasing temperature in the thermal treatment, the secondary cracks increase and propagate toward the direction of the main cracks under the loading effect. These research results can provide a reference for developing high-temperature reservoirs and the technology for fracture initiation in such reservoirs. [ABSTRACT FROM AUTHOR]

We have developed a general model to predict CO2 equilibrium solubility in aqueous tertiary amine solutions by relating the equilibrium constant (K1) to all relevant parameters in a logical function form. Testing our model, we measured CO2 equilibrium solubility data for N‐methylmorpholine (NMM) and N‐ethylmorpholine (NEM) across various conditions. Comparison with six existing models reveals our general model's superior predictive performance not only for NMM and NEM but also for an additional 10 tertiary amine solutions from literature, indicating its universality. Comprehensively considering the CO2 equilibrium solubility, amine dissociation constant (pKa) and the CO2 absorption heat, it is found that NMM and NEM may be promising desorption promoters enabling to reduce the energy cost. In short, it is expected the general model can be applied to more other tertiary amine systems. [ABSTRACT FROM AUTHOR]

In order to rapidly and accurately diagnose and identify nitrogen stress in rice, a field experiment was conducted. Taking the super rice variety 'Liangyoupei 9' as material, 4 treatments of nitrogen application (0, 210, 300, and 390 kgµhm-2) were set, and images of the first, second, and third leaves at the top of the rice plant were scanned and collected during the spikelet differentiation and the full heading stage. The SE block (squeeze-and- excitation block) module was added to each residual block of the ResNet34 in convolutional neural network (CNN), and the weight parameters trained on the ImageNet (ImageNet large scale visual recognition challenge) dataset were transferred to the nitrogen nutrition diagnosis model of rice. The feature extraction layer of ResNet34 was kept unchanged, and the pooling layer at the end of the model was replaced with a global average pooling layer. The improved network was used to extract features from rice images and train the optimal weight file. The results showed that the improved network achieved a testing accuracy of 98.13% during the spikelet differentiation stage and 99.46% during the full heading stage of rice. The convergence speed of the model was faster, and the accuracy was improved by more than 7% compared to the original network. Above results showed that it was feasible to add the SE block to the residual block of ResNet34 and use transfer learning to diagnose nitrogen nutrition in rice, which could effectively diagnose and identify the nitrogen nutrition of rice during the spikelet differentiation stage and the full heading stage. Above results provided reference for the diagnosis and identification of nutrient status in crops. [ABSTRACT FROM AUTHOR]