As PM2.5 affect human health, it is important to target tree planting in the role of reducing air pollution concentrations. PM2.5 capture capability of greening trees is associated with leaf morphology, while quantitative research is scanty. In this paper, the PM2.5 capture capability of 25 species in Beijing and Chongqing were examined by a chamber device. Groove proportion, leaf hair, stomatal density, and stomata size were selected as indexes of leaf morphology. Results showed that groove proportion and stomata size significantly relate to PM2.5 capture quantity, while no significantly positive correlations were found for leaf hairs and stomatal density. Broadleaf species are conducive to PM2.5 capture for their rich leaf morphology and have an edge over coniferous in PM2.5 capture per leaf area. However, coniferous had a larger PM2.5 capture capability per tree due to the advantage of a large leaf area. Significant difference existed between the species in Beijing and Chongqing due to the different leaf morphology. Urban greening trees are diverse and the structures are complicated. Complex ecological environment may lead to different morphology characteristics. Climate and pollution conditions should be considered when greening.

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Exploring and designing two-dimensional (2D) nanomaterials for armor-piercing protection has become a research focus. Here, by molecular dynamics simulation, we revealed that the ultralight monolayer covalent organic framework (COF), one kind of novel 2D crystalline polymer, possesses superior impactresistant capability under high-velocity impact. The calculated specific penetration energy is much higher than that of other traditional impact-resistant materials, such as steel, poly(methyl methacrylate), Kevlar, etc. It was found that the hexagonal nanopores integrated by polymer chains have large deformation compatibility resulting from flexible torsion and stretching, which can remarkably contribute to the energy dissipation. In addition, the deformable nanopores can effectively restrain the crack propagation, enable COF to resist multiple impacts. This work uncovers the extreme dynamic responses of COF under highvelocity impact and provides theoretical guidance for designing superstrong 2D polymer-based crystalline nanomaterials.

With the rapid development of mobile networks and the proliferation of mobile devices, Spatial Crowdsourcing (SC) has attracted the interest of industry and research groups. In addition to considering the specific spatial constraints in the existing research spatial crowdsourcing, each task has an effective duration, operational complexity, number of workers required, and incentive budget constraints. In this scenario, we studied the MQC-TA (Maximum Quality and Minimum Cost Task Assignment) problem. Firstly, the worker incentive model is established. The MQC-GAC algorithm is designed according to the MQC-TA problem to maximize the task completion quality and minimize the incentive budget. The algorithm combined the fast convergence of Genetic Algorithm and the positive feedback mechanism of Ant Colony Optimization Algorithm. Finally, the effectiveness and efficiency of the proposed method are verified by a comprehensive experiment on the data set. INDEX TERMS Spatial crowdsourcing, task assignment, MQC-TA problem, MQC-GAC algorithm.

With the depletion of surface resources, mining will develop toward the deep surface in the future, the objective conditions such as the mining environment will be more complex and dangerous than now, and the requirements for personnel and equipment will be higher and higher. The efficient mining of deep space is inseparable from movable and flexible production and transportation equipment such as scrapers. In the new era, intelligence is leading to the development trend of scraper (LHD), path tracking control is the key to the intelligent scraper (LHD), and it is also an urgent problem to be solved for unmanned driving. This paper describes the realization of the automatic operation of articulating the scraper (LHD) from two aspects, a mathematical model and trajectory tracking control method, and it focuses on the research of the path tracking control scheme in the field of unmanned driving, that is, an LQR controller. On this basis, combined with different intelligent clustering algorithms, the parameters of the LQR controller are optimized to find the optimal solution of the LQR controller. Then, the path tracking control of an intelligent LHD unmanned driving technology is studied, focusing on the optimization of linear quadratic optimal control (LQR) and the intelligent cluster algorithms AGA, QPSO, and ACA; this research has great significance for the development of the intelligent scraper (LHD). As mining engineers, we not only need to conduct research for practical engineering projects but also need to produce theoretical designs for advanced mining technology; therefore, the area of intelligent mining is the one we need to explore at present and in the future. Finally, this paper serves as a guide to starting a conversation, and it has implications for the development and the future of underground transportation.