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ObjectiveTo understand the research status of post-traumatic stress disorder （PTSD） at home and abroad in recent five years， and to grasp the research frontiers and hot spots in this field.MethodsTwo electronic databases， Web of Science and China National Knowledge Infrastructure （CNKI）， were retrieved for the literature published from January 1， 2017 to December 31， 2021. A total of 8 505 literatures were included， then the visualization analysis of the number of publications， authors， countries， institutions and keywords was conducted based on Microsoft Excel and CiteSpace software.Results① The number of publications in domestic and foreign showed an increasing trend in recent five years. ② In foreign literature， the top five countries in terms of the number of publications were the United States， the United Kingdom， China， Australia and Canada， with Canada having the highest centrality （0.18）. ③ Both domestic and foreign research institutions were dominated by universities. ④ In terms of the number of articles published， the top three foreign scholars were Bryant RA， Ressler KJ and Greenberg N， and the top three Chinese scholars were Wu Xinchun， Li Yuefeng， Yan Xingke and Zhang Guiqing （tied for the third place）. Compared with foreign authors， the number of articles published by Chinese scholars was relatively small. ⑤ In terms of research keywords， PTSD and depression were of more concern in both domestic and foreign.ConclusionIn recent five years， PTSD has been a hot topic at home and abroad， with both domestic and international studies focusing on PTSD and depression， and strengthening international exchanges may help promote progress in the field of PTSD research.

More attention is paid on the circulating fluidized bed (CFB) combustion technology because of its advantage in low cost emission control. Recently, great achievements have been realized in China. The development history of CFB combustion technology in China for decades is reviewed in this paper, from learning to independent technological innovation. High-performance CFB boilers, energy-saving CFB boilers and ultra-low emissions CFB boilers have be successively developed, while improving steam parameters and realizing large-scale. At present, China has become the largest equipment supplier and user of CFB boilers in the world. As an important part of China's coal-fired power generation system, important contributions have been made to reliable and cheap power supply and low-quality fuel consumption. For the carbon neutralization, coal will be indispensable in the security of power system as the guaranteed energy. As the main way of efficient and clean utilization of low calorific value coal and high sulfur anthracite, greater advantages of CFB boiler in deep peak regulation and rapid load changing flexibility should be exploited. To meet the peak regulating demand of high proportion of new energy consumption, pulverized coal CFB technology can be developed, distributed small-capacity high-parameter CFBs can be explored, and the potential of 0-100% load long-term combustion break and fast restart of CFB units can be explored, so as to further improve the operational flexibility of CFB units. On the basis of operation flexibility, the advantages of CFB boiler fuel flexibility are used to realize high-sulfur anthracite ultra-supercritical high-efficiency power generation and to achieve ultra-low emissions simultaneously. Furthermore, mixed fuel CFB power generation should be developed to realize the consumption of low-carbon combustible wastes such as municipal sludge, domestic waste, biomass and other inferior fuels from of coal mining and washing. In addition, ultra-low emission control technology under flexible operation and intelligent CFB power generation technology should be developed to assist the transformation and development of China's energy structure.

Let G be a nontrivial edge-colored connected graph. An edge-cut R of G is called a rainbow-cut if no two of its edges are colored the same. An edge-colored graph G is rainbow disconnected if for every two vertices u and v of G, there exists a u-v-rainbow-cut separating them. For a connected graph G, the rainbow disconnection number of G, denoted by rd(G), is defined as the smallest number of colors that are needed in order to make G rainbow disconnected. In this paper, we first determine the maximum size of a connected graph G of order n with rd(G) = k for any given integers k and n with 1 ≤ k ≤ n − 1, which solves a conjecture posed only for n odd in [G. Chartrand, S. Devereaux, T.W. Haynes, S.T. Hedetniemi and P. Zhang, Rainbow disconnection in graphs, Discuss. Math. Graph Theory 38 (2018) 1007–1021]. From this result and a result in their paper, we obtain Erdős-Gallai type results for rd(G). Secondly, we discuss bounds on rd(G) for complete multipartite graphs, critical graphs with respect to the chromatic number, minimal graphs with respect to the chromatic index, and regular graphs, and we also give the values of rd(G) for several special graphs. Thirdly, we get Nordhaus-Gaddum type bounds for rd(G), and examples are given to show that the upper and lower bounds are sharp. Finally, we show that for a connected graph G, to compute rd(G) is NP-hard. In particular, we show that it is already NP-complete to decide if rd(G) = 3 for a connected cubic graph. Moreover, we show that for a given edge-colored (with an unbounded number of colors) connected graph G it is NP-complete to decide whether G is rainbow disconnected.

Aiming at the problems of insufficient knowledge distillation efficiency,single stage training methods,complex training processes and difficult convergence of traditional knowledge distillation methods in image classification tasks,this paper designs a mutual learning knowledge distillation based on multi-stage multi-generative adversarial networks(MS-MGANs).Firstly,the whole training process is divided into several stages,teacher models of different stages are obtained to guide student models to achieve better accuracy.Secondly,the layer-wise greedy strategy is introduced to replace the traditional end-to-end training mode,and the layer-wise training strategy based on convolution block is adopted to reduce the number of parameters to be optimized in each iteration process,and further improve the distillation efficiency of the model.Finally,a generative adversarial structure is introduced into the knowledge distillation framework,with the teacher model as the feature discriminator and the student model as the feature generator,so that the student model can better follow or even surpass the performance of the teacher model in the process of continuously imitating the teacher model.The proposed method is compared with other advanced knowledge distillation methods on several public image classification data sets,and the experimental results show that the new knowledge distillation method has better performance in image classification.

Combining user reviews with user ratings to improve the performance of recommender system is the current mainstream research direction of recommender system.However,when user review data is sparse,the performance of most existing recommender systems will degrade to a certain extent.To solve this problem,this paper proposes a hybrid recommendation system (AMGNRS),which combines attention mechanism and gating networking based recommendation system.It use auxiliary comments generated by like-minded users to alleviate the sparsity of user comments.Firstly,a variety of mixed attention mechanism are combined to impove the feature extracting efficiency of user comments and grading.Then features are extracted by adaptive fusion of gated network,and features most relevant to user preference are selected.Finally,the higher order linear interaction of the neural factorization machine is used to derive the score prediction.By comparing the model with the current model with excellent performance on three real data sets,the results show that the problem of data sparsity is significantly alleviated and the effectiveness of the model is verified.