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

Coherent Beam-Beam Tune Shift of Unsymmetrical Beam-Beam Interactions with Large Beam-Beam Parameter

Coherent beam-beam tune shift of unsymmetrical beam-beam interactions was studied experimentally and numerically in HERA where the lepton beam has a very large beam-beam parameter (up to $\xi_y=0.272$). Unlike the symmetrical case of beam-beam interactions, the ratio of the coherent and incoherent beam-beam tune shift in this unsymmetrical case of beam-beam interactions was found to decrease monotonically with increase of the beam-beam parameter. The results of self-consistent beam-beam simulation, the linearized Vlasov equation, and the rigid-beam model were compared with the experimental measurement. It was found that the coherent beam-beam tune shifts measured in the experiment and calculated in the simulation agree remarkably well but they are much smaller than those calculated by the linearized Vlasov equation with the single-mode approximation or the rigid-beam model. The study indicated that the single-mode approximation in the linearization of Vlasov equation is not valid in the case of unsymmetrical beam-beam interactions. The rigid-beam model is valid only with a small beam-beam parameter in the case of unsymmetrical beam-beam interactions.Comment: 32 pages, 13 figure

Ad Hoc Quantum Network Routing Protocol based on Quantum Teleportation

Abstract-In this paper, a quantum communication routing protocol is designed for quantum ad hoc network. This protocol is on-demand routing based on EPR numbers shared by adjacent nodes, concerning that it is a limited source. When quantum channel is established, quantum states from one quantum device can be teleport to another even when they do not share EPR pairs wirelessly. Part of information transferred by classic channel can be dealt with using simple logics. In this way, the goal of safety communication between source and destination is realized, improving the weakness of ad hoc network such as Eavesdropping and Active attacks. In terms of time complexity, the mechanism transports a quantum bit in time almost the same as the quantum teleportation does regardless of the number of hops between the source and destination. Index Terms-EPR pair, quantum route, quantum entanglement, ad hoc network, quantum teleportation

Impervious surface change mapping with an uncertainty-based spatial-temporal consistency model: a case study in Wuhan city using Landsat time-series datasets from 1987 to 2016

Detailed information on the spatial-temporal change of impervious surfaces is important for quantifying the effects of rapid urbanization. Free access of the Landsat archive provides new opportunities for impervious surface mapping with fine spatial and temporal resolution. To improve the classification accuracy, a temporal consistency (TC) model may be applied on the original classification results of Landsat time-series datasets. However, existing TC models only use class labels, and ignore the uncertainty of classification during the process. In this study, an uncertainty-based spatial-temporal consistency (USTC) model was proposed to improve the accuracy of the long time series of impervious surface classifications. In contrast to existing TC methods, the proposed USTC model integrates classification uncertainty with the spatial-temporal context information to better describe the spatial-temporal consistency for the long time-series datasets. The proposed USTC model was used to obtain an annual map of impervious surfaces in Wuhan city with Landsat Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+), and Operational Land Imager (OLI) images from 1987 to 2016. The impervious surfaces mapped by the proposed USTC model were compared with those produced by the support vector machine (SVM) classifier and the TC model. The accuracy comparison of these results indicated that the proposed USTC model had the best performance in terms of classification accuracy. The increase of overall accuracy was about 4.23% compared with the SVM classifier, and about 1.79% compared with the TC model, which indicates the effectiveness of the proposed USTC model in mapping impervious surfaces from long-term Landsat sensor imagery

Neuroprotective Mechanisms of Lycium barbarum Polysaccharides Against Ischemic Insults by Regulating NR2B and NR2A Containing NMDA Receptor Signaling Pathways

Glutamate excitotoxicity plays an important role in neuronal death after ischemia. However, all clinical trials using glutamate receptor inhibitors have failed. This may be related to the evidence that activation of different subunit of NMDA receptor will induce different effects. Many studies have shown that activation of the intrasynaptic NR2A subunit will stimulate survival signaling pathways, whereas upregulation of extrasynaptic NR2B will trigger apoptotic pathways. A Lycium barbarum polysaccharide (LBP) is a mixed compound extracted from Lycium barbarum fruit. Recent studies have shown that LBP protects neurons against ischemic injury by anti-oxidative effects. Here we first reported that the effect of LBP against ischemic injury can be achieved by regulating NR2B and NR2A signaling pathways. By in vivo study, we found LBP substantially reduced CA1 neurons from death after transient global ischemia and ameliorated memory deficit in ischemic rats. By in vitro study, we further confirmed that LBP increased the viability of primary cultured cortical neurons when exposed to oxygen-glucose deprivation (OGD) for 4 h. Importantly, we found that LBP antagonized increase in expression of major proteins in the NR2B signal pathway including NR2B, nNOS, Bcl-2-associated death promoter (BAD), cytochrome C (cytC) and cleaved caspase-3, and also reduced ROS level, calcium influx and mitochondrial permeability after 4 h OGD. In addition, LBP prevented the downregulation in the expression of NR2A, pAkt and pCREB, which are important cell survival pathway components. Furthermore, LBP attenuated the effects of a NR2B co-agonist and NR2A inhibitor on cell mortality under OGD conditions. Taken together, our results demonstrated that LBP is neuroprotective against ischemic injury by its dual roles in activation of NR2A and inhibition of NR2B signaling pathways, which suggests that LBP may be a superior therapeutic candidate for targeting glutamate excitotoxicity for the treatment of ischemic stroke

Measuring River Wetted Width from Remotely Sensed Imagery at the Subpixel Scale with a Deep Convolutional Neural Network

River wetted width (RWW) is an important variable in the study of river hydrological and biogeochemical processes. Presently, RWW is often measured from remotely sensed imagery and the accuracy of RWW estimation is typically low when coarse spatial resolution imagery is used because river boundaries often run through pixels that represent a region that is a mixture of water and land. Thus, when conventional hard classification methods are used in the estimation of RWW, the mixed pixel problem can become a large source of error. To address this problem, this paper proposes a novel approach to measure RWW at the sub‐pixel scale. Spectral unmixing is first applied to the imagery to obtain a water fraction image that indicates the proportional coverage of water in image pixels. A fine spatial resolution river map from which RWW may be estimated is then produced from the water fraction image by super‐resolution mapping (SRM). In the SRM analysis, a deep convolutional neural network (CNN) is used to eliminate the negative effects of water fraction errors and reconstruct the geographical distribution of water. The proposed approach is assessed in two experiments, with the results demonstrating that the CNN based SRM model can effectively estimate sub‐pixel scale details of rivers, and that the accuracy of RWW estimation is substantially higher than that obtained from the use of a conventional hard image classification. The improvement shows that the proposed method has great potential to derive more accurate RWW values from remotely sensed imagery

Night-time lights are more strongly related to urban building volume than to urban area

A strong relationship between night-time light (NTL) data and the areal extent of urbanized regions has been observed frequently. As urban regions have an important vertical dimension, it is hypothesized that the strength of the relationship with NTL can be increased by consideration of the volume rather than simply the area of urbanized land. Relationships between NTL and the area and volume of urbanized land were determined for a set of towns and cities in the UK, the conterminous states of the USA and countries of the European Union. Strong relationships between NTL and the area urbanized were observed, with correlation coefficients ranging from 0.9282 to 0.9446. Higher correlation coefficients were observed for the relationship between NTL and urban building volume, ranging from 0.9548 to 0.9604; The difference in the correlations obtained with volume and with area was statistically significant at the 95% level of confidence. Studies using NTL data may be strengthened by consideration of the volume rather than just area of urbanized land