Abstract:Measuring the success of sustainable urban development has been difficult in the past. However, as this has become more important in the past few years, this paper develops an innovative sustainable urban development capacity measurement model based on principal component analysis (PCA) and Grey TOPSIS methodology, which has a significantly more comprehensive measurement, and reduces processing time and calculation difficulty. First, PCA is used to extract the main components that affect a city's sustainable development capacity. Then, the actual sustainable development capacity level is measured using Grey TOPSIS, from which the sustainable development capacity measurement value is then calculated. To prove the model's effectiveness and operability, it is then applied to measure the sustainable development capacity in 13 cities in Jiangsu province, China.

In order to safely and comfortably navigate in the complex urban traffic, it is necessary to make multi-modal predictions of autonomous vehicles for the next trajectory of various traffic participants, with the continuous movement trend and inertia of the surrounding traffic agents taken into account. At present, most trajectory prediction methods focus on prediction on future behavior of traffic agents but with limited, consideration of the response of traffic agents to the future behavior of the ego-agent. Moreover, it can only predict the trajectory of single-type agents, which make it impossible to learn interaction in a complex environment between traffic agents. In this paper, we proposed a graph-based heterogeneous traffic agents trajectory prediction model LSTGHP, which consists of the following three parts: (1) layered spatio-temporal graph module; (2) ego-agent motion module; (3) trajectory prediction module, which can realize multi-modal prediction of future trajectories of traffic agents with different semantic categories in the scene. To evaluate its performance, we collected trajectory datasets of heterogeneous traffic agents in a time-varying, highly dynamic urban intersection environment, where vehicles, bicycles, and pedestrians interacted with each other in the scene. It can be drawn from experimental results that our model can improve its prediction accuracy while interacting at a close range. Compared with the previous prediction methods, the model has less prediction error in the trajectory prediction of heterogeneous traffic agents.

More than half of the total production of tea [Camellia sinensis (L.) Kuntze] in China is produced in the Guizhou province. However, in this region soils generally have low soil fertility, so new techniques to improve the productivity and fertility are needed. The aim of this study was to evaluate the effects of application of seven types of humic acid‐based organic–inorganic compound fertilizer (HTOF) on tea quality and productivity. Two different cultivars of tea (cultivars Shiqiantaicha and Fudingdabaicha) were used as model plants. In the research, seven different HTOF fertilizers were tested at two sites. Application of the HTOF increased soil pH to a range closer to that most suitable for the growth of tea plants and relatively increased soil organic matter, ammonium‐N, inorganic P, and extractable K. In addition, HTOF increased tea bud weights and density. Application of HTOF‐1 also produced the best responses among all products tested. Findings from this study indicate that formula is recommended for application during the production of young C. sinensis tea plants.

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