Using the first principles particle swarm optimization algorithm for crystal structural prediction, we have predicted a hexagonalP63/mmcstructure of Tc2C.
By combining first-principles calculations with the particle swarm optimization algorithm, we predicted a hexagonal structure for TcB, which is energetically more favorable than the previously reported WC-type and Cmcm structures. The new phase is mechanically and dynamically stable, as confirmed by its phonon and elastic constants calculations. The calculated mechanical properties show that it is an ultra-incompressible and hard material. Meanwhile, the elastic anisotropy is investigated by the shear anisotropic factors and ratio of the directional bulk modulus. Density of states analysis reveals that the strong covalent bonding between Tc and B atoms plays a leading role in forming a hard material. Additionally, the compressibility, bulk modulus, Debye temperature, Grüneisen parameter, specific heat, and thermal expansion coefficient of TcB are also successfully obtained by using the quasi-harmonic Debye model.
Distributed acoustic sensing (DAS) is one of the most popular sensors for seismic acquisition. Compared with traditional seismic acquisition technology, DAS has the advantages of full-well coverage, high density, high efficiency, high sensitivity, low cost, strong resistance to high temperature and high pressure, and anti-electromagnetic field interference. However, the DAS vertical seismic profile (VSP) data are contaminated by strong noise interference, which brings challenges for practical applications and difficulties to seismic inversion and interpretation. A deep learning method named U-net with Global Context Block and Attention Block (GC-AB-Unet) is proposed to suppress the background noise and increase the data quality for DAS-VSP records without knowing any prior information. In GC-AB-Unet, several dropout layers are added to the U-net to avoid overfitting. Meanwhile, to speed up the network training, the residual units are set as the output of the network. Furthermore, GC-Block is introduced for better capturing shallow and deep features by extracting global context information. In addition, Attention Block is used to emphasize seismic event features and restrain irrelevant details in seismic data. We also construct a training dataset by utilizing the synthetic data and real noise of DAS-VSP data. The denoising results for both synthetic data and field DAS-VSP data show that compared to original U-net and Damped Rank Reduction (DRR) method, the GC-AB-Unet network is able to preserve the effective signals with almost no energy leakage while suppressing a large amount of background noise.
We theoretically study the high-order harmonics and single attosecond pulse generation from a pre-excited He+ ion in a multicycle two-color spatially inhomogeneous field. It is shown that the broadband supercontinuum spectrum and single quantum path selection can be realized, and then a single 15.2 as pulse is directly obtained. By appending a uv attosecond pulse to the two-color spatially inhomogeneous field at a proper time, the conversion efficiency of the high-energy part of the supercontinuum is improved by at least an order of magnitude, which enables the production of an intense single 17.3 as pulse.
Seismic data are often contaminated by random noise or even more complex types of noise, resulting in poor quality of seismic data with low signal-to-noise. Seismic random noise suppression is a crucial procedure in seismic data processing. Deep learning methods have been successfully applied to suppress seismic random noise. In this study, we propose a U-net based deep learning method to suppress seismic random noise. We add several dropout layers to the U-net to effectively avoid overfitting and set the output of the network as the residual units to enhance the training efficiency of our network. Moreover, the cosine similarity index is incorporated into the loss function to reserve the lateral continuity of geological structures. The denoising results of synthetic seismic data and field VSP data demonstrate that the proposed network has great performance in seismic random noise suppression in terms of both quantitative metrics and intuitive effects.
We theoretically present a method for generating an ultrabroad extreme ultraviolet (XUV) supercontinuum by using the combination of a multicycle chirped laser and a static electric field. At a low laser intensity, the spectral cutoff is extended to the 495th order harmonic, and the bandwidth of the supercontinuum spectrum is broadened to 535 eV. At a high laser intensity, the harmonic cutoff is enlarged to the 667th order, and a supercontinuum covering a bandwidth of 1035 eV is generated. In these two cases, the long quantum path is removed, and the short quantum path is selected. Especially for the relatively high laser intensity, an isolated 23-attosecond pulse with a bandwidth of about 170.6 eV is directly obtained. Finally, we also analyze the influences of the chirp parameter and the duration of the chirped pulse as well as the static field strength on the supercontinuum.
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