A multi-band semiclassical model for surface hopping quantum dynamics

In the paper we derive a semiclassical model for surface hopping allowing quantum dynamical non-adiabatic transition between different potential energy surfaces in which cases the classical Born-Oppenheimer approximation breaks down. The model is derived using the Wigner transform and Weyl quantization, and the central idea is to evolve the entire Wigner matrix rather than just the diagonal entries as was done previously in the adiabatic case. The off-diagonal entries of the Wigner matrix suitably describe the non-adiabatic transition, such as the Berry connection, for avoided crossings. We study the numerical approximation issues of the model, and then conduct numerical experiments to validate the model.Comment: 29 pages, 10 figure

Frozen Gaussian Sampling for Scalar Wave Equations

In this article, we introduce the frozen Gaussian sampling (FGS) algorithm to solve the scalar wave equation in the high-frequency regime. The FGS algorithm is a Monte Carlo sampling strategy based on the frozen Gaussian approximation, which greatly reduces the computation workload in the wave propagation and reconstruction. In this work, we propose feasible and detailed procedures to implement the FGS algorithm to approximate scalar wave equations with Gaussian initial conditions and WKB initial conditions respectively. For both initial data cases, we rigorously analyze the error of applying this algorithm to wave equations of dimensionality $d \geq 3$. In Gaussian initial data cases, we prove that the sampling error due to the Monte Carlo method is independent of the typical wave number. We also derive a quantitative bound of the sampling error in WKB initial data cases. Finally, we validate the performance of the FGS and the theoretical estimates about the sampling error through various numerical examples, which include using the FGS to solve wave equations with both Gaussian and WKB initial data of dimensionality $d = 1, 2$, and $3$

The Polar Wind Modulated by the Spatial Inhomogeneity of the Strength of the Earth's Magnetic Field

When the geomagnetic field is weak, the small mirror force allows precipitating charged particles to deposit energy in the ionosphere. This leads to an increase in ionospheric outflow from the Earth's polar cap region, but such an effect has not been previously observed because the energies of the ions of the polar ionospheric outflow are too low, making it difficult to detect the low‐energy ions with a positively charged spacecraft. In this study, we found an anticorrelation between ionospheric outflow and the strength of the Earth's magnetic field. Our results suggest that the electron precipitation through the polar rain can be a main energy source of the polar wind during periods of high levels of solar activity. The decreased magnetic field due to spatial inhomogeneity of the Earth's magnetic field and its effect on outflow can be used to study the outflow in history when the magnetic field was at similar levels.publishedVersio

The Role of SPARC Protein Expression in the Progress of Gastric Cancer

We aimed to investigate the expression of SPARC (secreted protein, acidic and rich in cysteine) in gastric cancer and its relationship with tumor angiogenesis and cancer cells proliferation. Protein expression of SPARC, VEGF, CD34 and Ki-67 in 80 cases of gastric cancer and 30 cases of normal gastric tissue was evaluated by immunohistochemistry. CD34 staining was used as an indicator of microvessel density (MVD). Ki-67 labeling Index (LI) indicated cancer cells proliferation. Statistical analysis was used to investigate its relationship with clinical characteristics, tumor angiogenesis and cancer cells proliferation. SPARC expression was mainly in the stromal cells surrounding the gastric cancer cells, and was statistically significant differences between gastric cancer and normal gastric tissue (P < 0.05). Both the expression of SPARC and VEGF were related to differentiation degree, clinical stage, Lauren classification and lymph node metastasis (P < 0.05). Expression of SPARC was significantly negatively correlated with the expression of VEGF and MVD in gastric cancer tissues. Expression of SPARC was also negatively correlated with Ki-67-LI. Our findings suggest that both the expression of SPARC and VEGF are closed to tumor angiogenesis in gastric cancer, SPARC inhibited tumor angiogenesis but VEGF promoted tumor angiogenesis. SPARC also inhibited cells proliferation of gastric cancer

Density and Magnetic Field Asymmetric Kelvin‐Helmholtz Instability

The Kelvin‐Helmholtz (KH) instability can transport mass, momentum, magnetic flux, and energy between the magnetosheath and magnetosphere, which plays an important role in the solar‐wind‐ magnetosphere coupling process for different planets. Meanwhile, strong density and magnetic field asymmetry are often present between the magnetosheath (MSH) and magnetosphere (MSP), which could affect the transport processes driven by the KH instability. Our magnetohydrodynamics simulation shows that the KH growth rate is insensitive to the density ratio between the MSP and the MSH in the compressible regime, which is different than the prediction from linear incompressible theory. When the interplanetary magnetic field (IMF) is parallel to the planet\u27s magnetic field, the nonlinear KH instability can drive a double mid‐latitude reconnection (DMLR) process. The total double reconnected flux depends on the KH wavelength and the strength of the lower magnetic field. When the IMF is anti‐parallel to the planet\u27s magnetic field, the nonlinear interaction between magnetic reconnection and the KH instability leads to fast reconnection (i.e., close to Petschek reconnection even without including kinetic physics). However, the peak value of the reconnection rate still follows the asymmetric reconnection scaling laws. We also demonstrate that the DMLR process driven by the KH instability mixes the plasma from different regions and consequently generates different types of velocity distribution functions. We show that the counter‐streaming beams can be simply generated via the change of the flux tube connection and do not require parallel electric fields

Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment

Frozen gaussian approximation for 3-D seismic wave propagation

We present a systematic introduction on applying frozen Gaussian approximation (FGA) to compute synthetic seismograms in 3-D earth models. In this method, seismic wavefield is decomposed into frozen (fixed-width) Gaussian functions, which propagate along ray paths. Rather than the coherent state solution to the wave equation, this method is rigorously derived by asymptotic expansion on phase plane, with analysis of its accuracy determined by the ratio of short wavelength over large domain size. Similar to other ray-based beam methods (e.g. Gaussian beam methods), one can use relatively small number of Gaussians to get accurate approximations of high-frequency wavefield. The algorithm is embarrassingly parallel, which can drastically speed up the computation with a multicore-processor computer station. We illustrate the accuracy and efficiency of the method by comparing it to the spectral element method for a 3-D seismic wave propagation in homogeneous media, where one has the analytical solution as a benchmark. As another proof of methodology, simulations of high-frequency seismic wave propagation in heterogeneous media are performed for 3-D waveguide model and smoothed Marmousi model, respectively. The second contribution of this paper is that, we incorporate the Snell's law into the FGA formulation, and asymptotically derive reflection, transmission and free surface conditions for FGA to compute high-frequency seismic wave propagation in high contrast media. We numerically test these conditions by computing traveltime kernels of different phases in the 3-D crust-over-mantle model.NRF (Natl Research Foundation, S’pore)Published versio

Frozen gaussian approximation for 3D seismic tomography

Three-dimensional (3D) wave-equation-based seismic tomography is computationally challenging in large scales and high-frequency regime. In this paper, we apply the frozen Gaussian approximation (FGA) method to compute 3D sensitivity kernels and seismic tomography of high-frequency. Rather than standard ray theory used in seismic inversion (e.g. Kirchhoff migration and Gaussian beam migration), FGA is used to compute the 3D high-frequency sensitivity kernels for travel-time or full waveform inversions. Specifically, we reformulate the equations of the forward and adjoint wavefields for the purpose of convenience to apply FGA, and with this reformulation, one can efficiently compute the Green's functions whose convolutions with source time function produce wavefields needed for the construction of 3D kernels. Moreover, a fast summation method is proposed based on local fast Fourier transform which greatly improves the speed of reconstruction as the last step of FGA algorithm. We apply FGA to both the travel-time adjoint tomography and full waveform inversion (FWI) on synthetic crosswell seismic data with dominant frequencies as high as those of real crosswell data, and confirm again that FWI requires a more sophisticated initial velocity model for the convergence than travel-time adjoint tomography. We also numerically test the accuracy of applying FGA to local earthquake tomography. This study paves the way to directly apply wave-equation-based seismic tomography methods into real data around their dominant frequencies.Published versio