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A direction of arrival based (DoA-based) beamforming of massive MIMO was proposed for high-speed railway scenarios.In order to guarantee that the optimal system capacity can be obtained during traveling,an iterative multi-beams scheme was proposed.It aimed to design different beams for different mobile carriage terminals (MCT) respectively.The proposal adaptively selected the number of antennas for each beam,and adaptively designed the number of beams according to system capacity.Moreover,the scheme didn’t need channel state information (CSI) that can reduce system implementation complexity and cost.Numerical results indicate that the proposed scheme is suitable for high-speed scenarios,the system performance can be improved more efficiently compared to conventional methods.

For the coexistence and increasing interference of satellite-terrestrial network and terrestrial wireless network,a typical scenario where the geostationary earth orbit (GEO) satellite-terrestrial network and the 4G mobile communication network coexist heterogeneously was analyzed.Besides,a multi-user cognitive system model that secondary satellite terminals interfere the primary multiple input multiple output (MIMO) 4G based stations was also proposed,with whose general signal processing was deduced.Meanwhile,digital beam forming (DBF) technology in 4G based station system was adopted to minimize the cognitive interference caused by multi-antennas and multi-users.And an optimal beam weight based on fixed azimuth interference (OBW-FAI) was proposed.Weight vector was only related to the azimuth of the interferences,thus the proposed algorithm does not need real-time and repeat calculations,and had small complexity.Finally,the numerical simulation results verify that the proposed system and algorithm can effectively reduce interference between satellite-terrestrial network and terrestrial wireless network to a certain extent.

The convergence performance of the fractional-order Darwinian particle swarm optimization (FO-DPSO) al-gorithm depends on the fractional-order α, and it can easily get trapped in the local optima. To overcome such shortcom-ing, an adaptive fractional-order Darwinian particle swarm optimization (AFO-DPSO) algorithm was proposed. In AFO-DPSO, both particle's position and velocity information were utilized adequately, together an adaptive acceleration coefficient control strategy and mutation processing mechanism were introduced for better convergence performance. Testing results on several well-known functions demonstrate that AFO-DPSO substantially enhances the performance in terms of convergence speed, solution accuracy and algorithm stability. Compared with PSO, HPSO, DPSO, APSO, FO-PSO, FO-DPSO and NCPSO, the global optimality of AFO-DPSO are greatly improved.

Network management and regulation system is a new arising appliance for the manageable,controllable and trustworthy demand of the Internet.The architecture design of the backbone link-level management and regulation system usually follows the core router,in which the separation of the data plane and the control plane could not meet the requirement of HW/SW data co-process,the rigid connection with closed building style could not satisfy the flexible identification of new applications.The loosely coupled switching HW/SW co-process architecture and the iterative looping HW/SW migration architecture were proposed,which could satisfy the above HW/SW co-process demand and the new application flexible identification demand.

Objective To analyze prevalence and genetic characteristics of clusterings/outbreaks of norovirus (NV) infection in Guangdong province in 2021. Methods We collected the information on all laboratory confirmed NV infection clusterings (3 correlated cases in 3 days at a venue) and outbreaks (20 correlated cases in 7 days at a venue) reported in Guangdong province throughout 2021. Anal swab/stool specimens of the reported cases were sampled for NV detection with quantitative real-time PCR (qRT-PCR). The NV GI and GII strains screened out from positive specimens were genotyped with PCR amplification, library preparation, gene sequencing and splicing. ResultsOf a total of 141 NV clusterings/outbreaks reported in the province during the year, 92.91% occurred in nurseries or schools and mainly during winter and spring seasons. For the genotyped NV strains isolated from specimens of 73 outbreaks (51.77% of all epidemics), 94.52% were NV GII, with GII.2[P16] and GII.4[P31] accounting for 39.13% and 23.19% of all NV strains, respectively. ConclusionIn Guangdong province during 2021, the norovirus clusterings/outbreaks occurred mainly in nurseries and schools in winter/spring seasons and the majority of the outbreaks were caused by norovirus GII.2[P16] and GII.4[P31].

Various graph-based algorithms for semi-supervised learning have been proposed in recent literatures. How-ever, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. The semi-supervised classification problem on query-document heteroge-neous information network which incorporate the bipartite graph with the content information from both sides is consid-ered. In order to strengthen the network structure, class information of sample nodes is introduced. A semi-supervised learning algorithm based on two frameworks including the novel graph-based regularization framework and the iterative framework is investigated. In the regularization framework, a new cost function to consider the direct relationship be-tween two entity sets and the content information from both sides which leads to a significant improvement over the baseline methods is developed. Experimental results demonstrate that proposed method achieves the best performance with consistent and promising improvements.