Tick-borne encephalitis (TBE) is endemic to Europe and some Asian countries and is prevalent in northeast China. We analyzed the epidemiology of TBE in China from 2007 to 2018. A total of 3,364 TBE cases were reported in mainland China from 2007 to 2018, for an annual incidence of 0.09 to 0.44/100,000. Among the TBE cases, 89.92% were reported in forest areas (41.94% in DaXingAnLing, 8.70% in XiaoXingAnLing, and 39.21% in ChangBaiShan) in northeast China. The TBE cases were primarily male with a proportion of 67.15% (2,259/3,364 cases) and in 40–49-year age group with a proportion of 31.89% (1,073/3,364 cases). The epidemiology of TBE differed slightly among the three forest regions. Domestic workers and forestry workers accounted for the most of the TBE cases in DaXingAnLing, and domestic workers and farmers in XiaoXingAnLing and ChangBaiShan, respectively. The TBE cases mainly occurred from April to August with a peak in June. The TBE laboratory confirmed rate in DaXingAnLing (84.14%, 1,189/1,413 cases) was highest, compared with XiaoXingAnLing and ChangBaiShan (13.99% and 11.37%, respectively). Moreover, the hospital with the highest laboratory confirmed rate (88.01%, 1,336/1,518 cases) was Inner Mongolia Forestry General Hospital of DaXingAnling region. Systematic enhanced TBE surveillance and a vaccination program are needed to improve the laboratory confirmed rate and reduce the incidence of TBE in northeast China.

Comparing with others, the supply chain of nuclear power plant (NPP) has its special characteristics, which not only on product safety, but also on the life cycle of supplier as well as requirement of environment, the selection of the appropriate supplier requires independent evaluation index system. For these reasons, based on establishing an evaluation index system, an integrated framework is proposed to approach the supplier selection problem in NPP supply chain systems utilizing analytical hierarchy process (AHP) and improved technique for order preference by similarity to ideal solution (TOPSIS). Then the ranking of the suppliers are determined according to their results. Finally, for application and verification, an empirical study is performed to demonstrate the integrated model and identify the suitable supplier(s).

This paper describes GPS multi-antenna device (one GPS receiver links multiple antennas) developed by authors, and the experimental results are presented. GPS has already proven to be an efficient tool for monitoring dam deformations and stability of high-risk slopes. It offers greater accuracy than other surveying techniques. However, GPS has its disadvantages when employing for slope and dam monitoring. The major drawback has been high cost due to large-scale GPS deployments are required in monitoring sites. The conventional GPS monitoring methods, where a permanent GPS receiver must be located at each point, have significant limitations of the cost. A new approach that a single GPS receiver links multiple antennas mounted at the monitoring points, has been employed to solve these problems in this paper. A dedicated switching device has been developed by authors for this approach. Field testing results show that the dedicated switching device for GPS multi-antenna system has excellent performances. Post-processing positioning accuracy is around 1-2 mm for the deformation monitoring of the Xiaolangdi dam on the Yellow River.

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Warning indicators are required for the real-time monitoring of the service conditions of dams to ensure safe and normal operations. Warnings are traditionally targeted at some “single point deformation” by deformation measuring points of concrete dam, and scientific warning theory on “overall deformation” measured is nonexistent. Furthermore, the influences of random factors are not considered. In this paper, the overall deformation of the dam was seen as a deformation system of single interactional observation points with different contribution degrees. The spatial deformation entropy, which describes the overall deformation, was established and the fuzziness indicator that measures the influence of complex random factors on monitoring values according to cloud theory was constructed. On this basis, multistage warning indicators of “spatial deformation” that consider fuzziness and randomness were determined. Analysis showed that the change law of information entropy of the dam’ overall deformation is identical to the real change law of the dam; thus, it reflects the real deformation state of the dam. Moreover, the identified warning indicators improved the warning ability of concrete dams.

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Summary Considering the limitations of the traditional hydraulic‐seasonal‐time (HST) model, this study proposes a hybrid modeling method for the deformation prediction of high concrete dams during the operational period. First, the elastic finite element (FE) method is applied to simulate the interactive effects of structural properties, topography, geology, and high hydrostatic load on the deformation behaviour of high concrete dams in operating conditions. The hybrid model of hydrostatic pressure deformation is established. The hybrid hydraulic‐seasonal‐time (HHST) model is proposed. Second, the self‐adaptive stochastic inertia weight, dynamic learning factors, and velocity and position parameters are introduced to improve the particle swarm optimization (PSO) algorithm. The hybrid prediction approach is developed through the comprehensive application of the HHST model and the improved PSO algorithm. The proposed methodology is adopted for the Jinping I project, which is the highest concrete arch dam in the world. The analysis results indicate that the model accuracy is good and that the model performance is promoted.