Equitable evaluation of supply-demand and layout optimization of urban park green space in high-density linear large city.

Equitable and effective planning of urban park green spaces (UPGSs) is an important way to promote green and healthy urban development and improve citizens' quality of life. However, under the background of rapid urbanization, linear large cities, with their unique spatial forms and high-density population agglomerations, have brought special challenges for the planning and management of urban public green spaces. This study takes Lanzhou, a typical representative of high-density linear large cities in China, as a case study. Based on the improvement of the traditional Gaussian Two-Step Floating Catchment Area method (G2SFCA), combined with the Gini coefficient and the Lorentz curve, the social equity and spatial equity of UPGS supply-demand in the central urban area of Lanzhou were evaluated at the city and district scales. Meanwhile, the areas with shortage of UPGS supply-demand were accurately identified as the key areas for future optimization. The results show that: (1) There are significant differences in the equity of UPGS supply-demand in the linear large Lanzhou at the social and spatial levels, and most UPGS resources are enjoyed by a few people; (2) The spatial accessibility of UPGSs has an obvious "string of beads" distribution Characteristics, and the areas with high accessibility are mainly concentrated along rivers; (3) The equity of UPGS supply-demand exhibits a spatial gradient effect, which is characterized by a circle distribution. From the inside to the outside, it is as follows: good supply-dense population, good supply-sparse population, supply shortage-dense population, supply shortage-sparse population. Finally, based on the concept of "progressive micro-regeneration" and the Location Allocation model (LA), the optimal sites for new UPGSs were determined, maximizing the equity of UPGS supply-demand. This provides a practical reference for relevant management departments to optimize park layouts in the future.

Advances in Resistance Welding of Fiber-Reinforced Thermoplastics.

Fiber-reinforced thermoplastics (FRTPs) have become a new generation of lightweight materials due to their superior mechanical properties, good weldability and environmental resistance, potential for recycling, etc. The market for FRTPs is expected to grow at a compound annual growth rate (CAGR) of 7.8% from 2022 to 2030. Many researchers have been trying to solve the problems in their processing and joining process, and gradually expand their application. Resistance welding is one of the most suitable techniques to join FRTPs. This paper summarizes the research progress of FRTP resistance welding in terms of the basic process of FRTP resistance welding, factors affecting joint performance, joint failure behavior, numerical simulation, weld quality control, and resistance welding of thermoplastic/thermoset composites. The objective of this paper is to provide a deeper insight into the knowledge of FRTP resistance welding and provide reference for the further development and application of FRTP resistance welding.

Exploration of street space architectural color measurement based on street view big data and deep learning-A case study of Jiefang North Road Street in Tianjin.

Urban space architectural color is the first feature to be perceived in a complex vision beyond shape, texture and material, and plays an important role in the expression of urban territory, humanity and style. However, because of the difficulty of color measurement, the study of architectural color in street space has been difficult to achieve large-scale and fine development. The measurement of architectural color in urban space has received attention from many disciplines. With the development and promotion of information technology, the maturity of street view big data and deep learning technology has provided ideas for the research of street architectural color measurement. Based on this background, this study explores a highly efficient and large-scale method for determining architectural colors in urban space based on deep learning technology and street view big data, with street space architectural colors as the research object. We conducted empirical research in Jiefang North Road, Tianjin. We introduced the SegNet deep learning algorithm to semantically segment the street view images, extract the architectural elements and optimize the edges of the architecture. Based on K-Means clustering model, we identified the colors of the architectural elements in the street view. The accuracy of the building color measurement results was cross-sectionally verified by means of a questionnaire survey. The validation results show that the method is feasible for the study of architectural colors in street space. Finally, the overall coordination, sequence continuity, and primary and secondary hierarchy of architectural colors of Jiefang North Road in Tianjin were analyzed. The results show that the measurement model can realize the intuitive expression of architectural color information, and also can assist designers in the analysis of architectural color in street space with the guidance of color characteristics. The method helps managers, planners and even the general public to summarize the characteristics of color and dig out problems, and is of great significance in the assessment and transformation of the color quality of the street space environment.