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Underwater deep cement mixing pile (DCM) has been widely used as a soft soil foundation reinforcement treatment technology. It is the first example in China that DCM is applied to the foundation of immersed tunnel on a large scale in the Shenzhen-Zhong shan Link Project. This paper focuses on the research and discussion of the whole process of underwater DCM application from the aspects of design scheme, construction technology, post-construction detection and settlement monitoring. The preliminary design scheme was optimized, the DCM construction technology and automatic operation system under complex geological conditions were improved, and a set of post-construction inspection and evaluation system was formed. The monitor原ing results show that the bearing capacity of the immersed tube foundation is reliable, the differential settlement is controllable, and the construction technology is feasible, which can provide relevant reference for similar projects. [ABSTRACT FROM AUTHOR]

Predicting the future activity location and trajectory of residents can provide essential information for smart urban management such as epidemic control, traffic facilitation, public security, etc. However, the current personal location prediction methods generally focus on the mining of individual’s historical travel patterns, and seldom consider the feature of different travel stay points. This paper aims to propose a location prediction model utilizing stay point feature extraction. The model firstly constructs the historical trajectory links based on trajectory data, performs the location discovery rules to transform historical trajec⁃ tory links into stay point links and clusters the stay points to form clustering links. Secondly, the model ex⁃ tracts the feature information (entry time, departure time, weather and land use) from different stay points and extracts the space feature from clustering links. Finally, the cluster links with feature information is in⁃ troduced into long short ⁃ term memory (LSTM) network for customization to implement the personal loca⁃ tion prediction capability. By using a 23⁃day trajectory location data of more than 3 million volunteer users in Shenzhen city, China. The results show that the location prediction F⁃score of our proposed model is bet⁃ ter than variable order Markov model (about 5.5% performance gains) and the traditional N⁃order Markov model (about 7% performance gains). The model also introduces approximately 6.6% performance gains by utilizing the temporal and weather features of stay points. The model shows the capability of utilizing travel stay point feature for personal next location prediction. [ABSTRACT FROM AUTHOR]

There are 13,000 cross-boundary students studying in Hong Kong while living in Shenzhen. They cross the boundary every day but they cannot enjoy most social welfare services either in Hong Kong or in Mainland China. Having a unique socioeconomic and cultural background, these children encounter difficulties in adjusting to the school life in Hong Kong. Living in Shenzhen, their voices are not heard and their needs are not met. This paper will introduce the background and needs of the cross-boundary students and the services provided by the International Social Service Hong Kong Branch. Suggestions for effective policy provisions and social services to improve the life of cross-boundary students will also be presented. [ABSTRACT FROM AUTHOR]