"The safety and health of children are essential issues not only for households but also for society. Missing Child is a severe strike to a family. It causes severe trauma memory and affects family members both psychologically and physically. Incidents like this also spread a feeling of fear among the local community. Many efforts have been made to help with this problem, such as the Amber Alert system, the location-tracking functions in the operating system in smartphones. There are also many safety apps in the app stores trying to prevent this kind of incident. However, there are still things that these apps do not cover: What should the parents do when they lose contact with their children and feel worried? This project explored ways to provide guidance for parents and tried to find a way to balance the privacy-safety tradeoff."--Abstract.

China contributes the largest share of cropland's greenhouse gas (GHG) emissions globally. Processed-based biogeochemical models are useful tools to simulate GHG emissions from cropping systems. However, model comparisons are necessary to provide information for the application of models under different climate, soil, and crop conditions. In this study, two widely-used models (DayCent and DNDC) were evaluated and compared under four main cropping systems in China. The field observations from nine experiments were used for model calibration and validation. The DayCent and DNDC models simulated daily and seasonal CH4 emissions from early rice-late rice and rice-wheat cropping systems reasonably well (r2≥0.49 for daily simulation and nRMSE≤52.9% for seasonal simulation). Both models were able to satisfactorily predict seasonal N2O emissions from maize-wheat fields (0.6≤d≤0.8), but overestimated most daily N2O fluxes at fertilisation and irrigation events. Significantly positive relationships were found between simulated and observed cumulative N2O fluxes in spring maize growing season (0.61≤ r2≤0.85). The DNDC showed smaller differences in simulated and observed cumulative GHG emissions for spring maize and double rice, while DayCent showed better performance on estimating N2O and CH4 for maize-wheat and rice-wheat. This study shows that both models have strengths and weaknesses under a variety of cropping systems and growing regions, which are important to consider when choosing a model for a crop/region-specific simulation.