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

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 evolution of carbon footprint in the yangtze river delta city cluster during economic transition 2012-2015

China has been undergoing an industrial transformation, shifting from an energy-intensive growth pattern. As the most developed region in China, the Yangtze River Delta (YRD) city cluster is leading the industrial trans- formation. However, the impact of the industrial transformation on carbon footprints in the YRD cities is unclear. By a city-level environmentally extended input-output model, we quantify the carbon footprint of 41 cities in the YRD city cluster for 2012 and 2015 and capture the socioeconomic driving forces of the change by structural decomposition analysis (SDA). The results show that the carbon footprint in 41 YRD cities increased from 1179.4 Mt (14.8% of China’s total) to 1329.6 Mt (16.6%) over the period. More than 60% of the footprint concentrated on the 10 largest cities, and the construction sector made the largest contribution, especially in service-based megacities. The change of production structure drove down carbon footprints in YRD cities, except light in- dustry cities and service-based cities. The industrial transfers from the coastal to inland regions result in carbon leakage, where one-third of the carbon footprint is embodied in the trade. We also find the economic recession during the transition period decreased carbon emissions by 154.2 Mt in the YRD city cluster, where the value- added rate in the YRD cities declined over the transition period, especially in service-based cities. The study highlights the positive effects of industrial transformation on low carbon transition, despite being highly heterogenous for cities