The effect of closed classical orbits on quantum spectra: Ionization of atoms in a magnetic field

A quantitative theory of oscillatory spectra for atoms in a magnetic field is developed. When an atom is placed in a magnetic field, and absorption spectrum into states close to the ionization threshold is measured, it is found that the absorption as a function of energy is a superposition of many sinusoidal oscillations. Such interesting and surprising phenomenon are fully explained and described by the theory.;The theory is based on three approximations: (1) Near the atomic nucleus, the diamagnetic field is negligible. (2) Far from the nucleus, the wave propagates semiclassically. (3) Waves returning to the nucleus are similar to (cylindrically-modified) Coulomb-Scattering waves. Using these approximations, together with the simple physical picture of absorption process, formula is derived for the transition rate as a function of final states energy.;The main result is that the transition rate is equal to the sum of two very different kinds of contributions. The first is the averaged transition rate in the absence of the magnetic field, which is a smooth function of energy; the second is itself a sum over many oscillations. Each oscillation is closely associated with a band of wave, initially going out from the nucleus, propagating along a family of trajectories, and finally returning to the vicinity of the nucleus. Because in the center of the family of trajectories is a closed orbit going from the nucleus and returning to the nucleus, we say a closed orbit makes an oscillatory contribution to the absorption spectrum .;Formulas and algorithms are derived and specified for the calculations of the spectrum from the initial quantum state, dipole polarization, and the properties of the closed classical orbits. Good agreements with experiments were found. Very detailed interpretations are obtained

An analysis of coupling coordination relationship between regional economy and transportation: empirical evidence from China

The increasing discrepancy between a regional economy and transportation imposes higher requirements for their coordinated development. This paper utilized the coupling coordination degree (CCD) model and entropy method (EM) to quantitatively study the coupling coordination state between regional economy and transportation and its spatial distribution of 30 provinces in China from 2004 to 2017. The results show the following: (1) The comprehensive level of regional economy and transportation development in China has shown a growing trend, and the development of regional economy is faster than that of the transportation. (2) The CCD between regional economy and transportation in China is changing from incoordination to high-level coordination. The imbalance of CCD levels among the regions studied varies significantly. The CCD in the eastern regions is slightly higher than that in the central, western, and northeast regions. (3) In the region with a higher CCD, the discrepancy between the development of regional economy and transportation is higher than that in other regions. Moreover, suggestions are provided to promote the coordinated development from a regional perspective. This study provides references to policymakers to help them properly plan and design transportation systems while considering the regional economy, thus stimulating the coordinated and sustainable development between regional economy and transportation

A hybrid LSTM neural network for energy consumption forecasting of individual households

Irregular human behaviors and univariate datasets remain as two main obstacles of data-driven energy consumption predictions for individual households. In this study, a hybrid deep learning model is proposed combining an ensemble long short term memory (LSTM) neural network with the stationary wavelet transform (SWT) technique. The SWT alleviates the volatility and increases the data dimensions, which potentially help improve the LSTM forecasting accuracy. Moreover, the ensemble LSTM neural network further enhances the forecasting performance of the proposed method. Verification experiments were performed based on a real-world household energy consumption dataset collected by the 'UK-DALEat project. The results show that, with a competitive training efficiency, the proposed method outperforms all compared state-of-art methods, including the persistent method, support vector regression (SVR), long short term memory (LSTM) neural network and convolutional neural network combining long short term memory (CNN-LSTM), with different step sizes at 5, 10, 20 and 30 minutes, using three error metrics

Lobectomy with Bronchoplasty and Reconstruction of Pulmonary Artery by Minitrauma-technique for Lung Cancer

Background and objective To research the effect and practicalbility of lobectomy with bronchoplasty and reconstruction of pulmonary artery by minitrauma-technique for lung cancer. Methods We retrospectibely reviewed our experience on 61 cases being lobectomy with bronchoplasty and bronchoplasty with or without video assisted thoracic small incision surgery for lung cancer from July 2005 to June 2009 from Shandong Provincal Hospital and 46 cases simultaneously by routine posterolateral incision. All patients whose bronchus and/or pulmonary artery were involved underwent the operation and experienced the bronchial sleeve/wedge resection or reconstruction of the pulmonary artery. Results All patients were done operation successfully and there were no operative mortality and no occurrence of anastomosis stenosis as well as fistula. The small incisions’ length was from 8 cm-15 cm while the routine posterolateral incision’s length was 25 cm-35 cm. The patients done the operation of small incision had less postoperative shoulder joint dysfunction and had better quality of life compaired to the patients done the routine posterolateral incision. Conclusion Lobectomy with bronchoplasty and reconstruction of pulmonary artery by minitrauma-technique for lung cancer could finished the same work with the traditional thoracic lateral incision and had less trauma, less pain, less recovery time

Synthetic Aperture Radar Image Change Detection via Layer Attention-Based Noise-Tolerant Network

Recently, change detection methods for synthetic aperture radar (SAR) images based on convolutional neural networks (CNN) have gained increasing research attention. However, existing CNN-based methods neglect the interactions among multilayer convolutions, and errors involved in the preclassification restrict the network optimization. To this end, we proposed a layer attention-based noise-tolerant network, termed LANTNet. In particular, we design a layer attention module that adaptively weights the feature of different convolution layers. In addition, we design a noise-tolerant loss function that effectively suppresses the impact of noisy labels. Therefore, the model is insensitive to noisy labels in the preclassification results. The experimental results on three SAR datasets show that the proposed LANTNet performs better compared to several state-of-the-art methods. The source codes are available at https://github.com/summitgao/LANTNetComment: Accepted by IEEE Geoscience and Remote Sensing Letters (GRSL) 2022, code is available at https://github.com/summitgao/LANTNe

The impact of new digital infrastructure on green total factor productivity

As a new engine driving economic development, new digital infrastructure plays a significant role in enhancing green total factor productivity. Based on 2011–2020 panel data covering 30 Chinese provinces, this study empirically investigates the effects and mechanisms of new digital infrastructure on green total factor productivity. The results show that new digital infrastructure can significantly improve regional green total factor productivity, and this conclusion remains valid after a series of robustness tests and regressions of instrumental variables. Further mechanism research shows that new digital infrastructure indirectly promotes the growth of green total factor productivity by improving capital misallocation and driving technological innovation, while there is no mediating mechanism of labor misallocation. In addition, there is significant heterogeneity in the impact of new digital infrastructure on green total factor productivity. Especially during periods of high government attention, in the eastern regions, and in areas with higher levels of human capital, the positive incentive effect of new digital infrastructure is more significant. This study provides empirical evidence and policy references for promoting and amplifying the green growth effects of new digital infrastructure