Extreme rainfall is a common triggering factor of landslide disasters, for infiltration and pore water pressure propagation can reduce suction stress and shear strength at the slip surface. The subsurface hydrological model is an essential component in the early-warning system of rainfall-triggered landslides, whereas soil moisture and pore water pressure simulated by the Darcy–Richards equation could be significantly affected by uncertainties in soil hydraulic parameters. This study conducted an inverse analysis of in situ measured soil moisture in an earthquake-induced landslide deposit, and the soil hydraulic parameters were optimized with the Differential Evolution Markov chain Monte Carlo method (DE-MC). The DE-MC approach was initially validated with a synthetic numerical experiment to demonstrate its effectiveness in finding the true soil hydraulic parameters. Besides, the soil water characteristic curve (SWCC) and hydraulic conductivity function (HCF) described with optimized soil hydraulic parameter sets had similar shapes despite the fact that soil hydraulic parameters may be different. Such equifinality phenomenon in inversely estimated soil hydraulic parameters, however, did not affect the performance of simulated soil moisture dynamics in the synthetic numerical experiment. The application of DE-MC to a real case study of a landslide deposit also indicated satisfying model performance in terms of accurate match between the in situ measured soil moisture content and ensemble of simulations. In conclusion, based on the satisfying performance of simulated soil moisture and the posterior probability density function (PDF) of parameter sets, the DE-MC approach can significantly reduce uncertainties in specified prior soil hydraulic parameters. This study suggested the integration of the DE-MC approach with the Darcy–Richards equation for an accurate quantification of unsaturated soil hydrology, which can be an essential modeling strategy to support the early-warning of rainfall-triggered landslides.

Cultural self-consciousness" is a new proposition put forward by sociologist Fei Xiaotong. He advocated treating the foreign culture with a concept of harmony in diversity and mutual perfection. Reflection is the core of "cultural self-consciousness" reflected, the culture reflection on their cultural sources, gains and losses based on cultural self-confidence, insight into the advantages and disadvantages of their own culture on the basis of rational thinking. Economic globalization is the inevitable trend of the development of productive forces, in cross cultural communication, should be good at using the "others" perspective to understand the world, we should oppose "cultural separatism", but also against the "cultural hegemony". Arouse the cultural self-consciousness of the whole nation,to make the cultural self-consciousness rise to the rational level, will greatly promote the development of national culture.

Abstract. "Cultural self-consciousness" is a new proposition put forward by sociologist Fei Xiaotong. He advocated treating the foreign culture with a concept of harmony in diversity and mutual perfection. Reflection is the core of "cultural self-consciousness" reflected, the culture reflection on their cultural sources, gains and losses based on cultural self-confidence, insight into the advantages and disadvantages of their own culture on the basis of rational thinking. Economic globalization is the inevitable trend of the development of productive forces, in cross cultural communication, should be good at using the "others" perspective to understand the world, we should oppose "cultural separatism", but also against the "cultural hegemony". Arouse the cultural self-consciousness of the whole nation,to make the cultural self-consciousness rise to the rational level, will greatly promote the development of national culture.

While recycling facilities have been significantly upgraded in China, but the effectiveness of these facilities in improving waste management needs to be evaluated. Here, we conducted a nationwide survey by directly taking photographs of the inside of individual waste containers over 11 cities across China. We found that waste from recycling and non-recycling containers generally comprised similar materials. The corresponding waste features extracted by machine learning models tend to be well-mixed but clearly separated after removing the misplaced items, demonstrating an objective means for quantifying the accuracy of waste-sorting process. We therefore proposed the nationwide scale-up of this automated machine learning system, which along with additional incentive programs for better waste-sorting behaviors, may help improve waste management.