The analysis of the LHCb data on \begin{document}$X(6900)$\end{document} found in the di-\begin{document}$J/\psi$\end{document} system was performed using a momentum-dependent Flatté-like parameterization. The use of the pole counting rule and spectral density function sum rule provides consistent evidence that both confining and molecular states are possible. Alternatively, the nature of \begin{document}$X(6900)$\end{document} cannot be distinguished if only the di-\begin{document}$J/\psi$\end{document} experimental data with current statistics are available. Nevertheless, we found that the lowest state in the di-\begin{document}$J/\psi$\end{document} system likely has the same quantum numbers as \begin{document}$X(6900)$\end{document}, and \begin{document}$X(6900)$\end{document} is probably not interpreted as a \begin{document}$J/\psi-\psi(2S)$\end{document} molecular state. 相似文献
Optical coherence tomography (OCT) images coupled with many learning techniques have been developed to diagnose retinal disorders. This work aims to develop a novel framework for extracting deep features from 18 pre-trained convolutional neural networks (CNN) and to attain high performance using OCT images. In this work, we have developed a new framework for automated detection of retinal disorders using transfer learning. This model consists of three phases: deep fused and multilevel feature extraction, using 18 pre-trained networks and tent maximal pooling, feature selection with ReliefF, and classification using the optimized classifier. The novelty of this proposed framework is the feature generation using widely used CNNs and to select the most suitable features for classification. The extracted features using our proposed intelligent feature extractor are fed to iterative ReliefF (IRF) to automatically select the best feature vector. The quadratic support vector machine (QSVM) is utilized as a classifier in this work. We have developed our model using two public OCT image datasets, and they are named database 1 (DB1) and database 2 (DB2). The proposed framework can attain 97.40% and 100% classification accuracies using the two OCT datasets, DB1 and DB2, respectively. These results illustrate the success of our model. 相似文献
Community detection has become an important methodology to understand the organization and function of various real-world networks. The label propagation algorithm (LPA) is an almost linear time algorithm proved to be effective in finding a good community structure. However, LPA has a limitation caused by its one-hop horizon. Specifically, each node in LPA adopts the label shared by most of its one-hop neighbors; much network topology information is lost in this process, which we believe is one of the main reasons for its instability and poor performance. Therefore in this paper we introduce a measure named weighted coherent neighborhood propinquity (weighted-CNP) to represent the probability that a pair of vertices are involved in the same community. In label update, a node adopts the label that has the maximum weighted-CNP instead of the one that is shared by most of its neighbors. We propose a dynamic and adaptive weighted-CNP called entropic-CNP by using the principal of entropy to modulate the weights. Furthermore, we propose a framework to integrate the weighted-CNP in other algorithms in detecting community structure. We test our algorithm on both computer-generated networks and real-world networks. The experimental results show that our algorithm is more robust and effective than LPA in large-scale networks. 相似文献
Semipolar (11\bar 2 \bar 2) ZnO was successfully grown on (112) LaAlO3/(LaAlO3)0.29(Sr2AlTaO6)0.35 substrate by pulsed laser deposition. The epitaxial relationship is [11\bar 23]_{\rm ZnO} // [11\bar 1]_{\rm LAO/LSAT} with the polar axis of [000\bar 1]_{\rm ZnO} pointing to the surface. For ZnO films with thickness of 1.6 μm, the threading dislocation density is ～1 × 109 cm–2, and the density of basal stacking faults is below 1 × 104 cm–1. The (11\bar 2 \bar 2) ZnO exhibits strong D0X emissions with a FWHM of 9 meV and very few green–yellow emissions in the low‐temperature (10 K) and room‐temperature photoluminescence spectra, respectively.
As more efficient and reliable torches for thermal plasma generation have become available in recent years, the use of thermal plasma as an energy source for pyrolysis/gasification has attracted much interest, and special attention has been paid to waste treatment for resource and energy recovery. Plasma pyrolysis/gasification systems have unique features such as the extremely high reaction temperature and ultra-fast reaction velocity compared to traditional pyrolysis/gasification systems. Plasma pyrolysis/gasification is therefore acknowledged as a novel pyrolysis/gasification technology with great potential in solid waste disposal. This paper gives a comprehensive review on the development of fundamental researches on plasma pyrolysis/gasification systems including direct current (DC) arc plasma system and radio frequency (RF) plasma system with an emphasis on reactor design such as plasma fixed/moving bed reactor system, plasma entrained-flow bed reactor system and plasma spout-fluid bed reactor system. 相似文献
