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In the Global Positioning System (GPS), the receiver hardware-induced code and phase biases are important factors affecting precise timing, precise modeling of ionosphere and un-difference ambiguity resolution. In the estimation and modeling of ionospheric electron content, it is generally assumed that the GPS receiver hardware biases are constant amounts, without considering the possible fluctuations and influencing factors. Therefore, to analyze the time-varying characteristics of receiver hardware biases can help improve the accuracy and reliability of the ionospheric electron content estimation. In this paper, the time-varying characteristics of single-epoch and single-day solved GPS receiver differential code bias (DCB) and differential phase bias (DPB) are analyzed, and the correlation between the variation of receiver hard⁃ ware delay and the change of ambient temperature is explored experimentally. The results indicate that re⁃ ceiver DCB will mutate after a restart. And after restarting, the receiver DCB and DPB have a changing process, which takes about 25 min, afterwards they keep stabilized. Also, it’s worth noting that the receiv⁃ er DCB and DPB cannot be stable for a long time. The experimental results show that the change of receiv⁃ er DCB and DPB can reach about 0.8 m and 4 mm in 2-3 h. In addition, the fluctuation of receiver hard⁃ ware delay has a strong correlation with the change of the vicinity temperature. [ABSTRACT FROM AUTHOR]

Objectives: Emergency response for biohazard is an important part of biosafety support, which need to be organized and implemented around the emergency response plan. The current knowledge organization of emergency response plan is incomplete and the existing data models are unable to fully meet the needs of biohazard situation spatiotemporal analysis.Methods: A biohazard emergency response geo‐ontology model (BEROM) was proposed based on the ABC (antecedent‐behavior‐consequence) ontology, and the rule set for emergency decision reasoning was built. Results: Compared with the traditional methods, the proposed model can better integrate event information and plan knowledge, and give more effective emergency decision ‐ making opinions by reasoning. Conclusions: The intelligent construction of emergency response plan is realized, which provides a new knowledge model for emergency decision‐making, and verifies the rationality and effectiveness of the model. [ABSTRACT FROM AUTHOR]