Many plants exhibit heterophylly; the spatially and temporally remarkable ontogenetic differences in leaf morphology may play an adaptative role in their success under diverse habitats. Thus, this study aimed to gain insights into differences in leaf functional traits of heterophyllous Syringa oblata Lindl., which has been widely used as an ornamental tree around the world under different light intensities in East China. No significant differences existed in specific leaf area (SLA) between lanceolate-and heart-shaped leaves. Differences in the investment per unit of light capture surface area deployed between lanceolate-and heart-shaped leaves may be not obvious. This may be attributing to the fact that single leaf wet and dry weight of heart-shaped leaves were significantly higher than those of lanceolate leaves but leaf length and leaf thickness of heart-shaped leaves were significantly lower than those of lanceolate leaves. The SLA of shade trees was significantly higher than that of sun trees. The investment per unit of light capture surface of shade trees was lower than that of sun trees, making it possible to increase light capture and use efficiency in low-light environments. The phenotypic plasticity of most leaf functional traits of lanceolate leaves was higher than those of heart-shaped leaves because the former is the juvenile and the latter is the adult leaf shape during the process of phylogenetic development of S. oblate. The higher range of phenotypic plasticity of leaf thickness and leaf moisture for sun trees may be beneficial to obtain a more efficient control of water loss and nutrient deprivation in highlight environments, and the lower range of phenotypic plasticity of single leaf wet and dry weight, and SLA for shade trees may gain an advantage to increase resource (especially light) capture and use efficiency in low-light environments. In brief, the successfully ecological strategy of plants is to find an optimal mode for the trade-off between various functional traits to obtain more living resources and achieve more fitness advantage as much as possible in the multivariate environment.

Plastic greenhouses (PGs) are widely built near cities in China to produce vegetables and fruits. In order to promote sustainable agriculture, rural landscape construction, and better manage water resources, numerous remote sensing methods have been developed to identify and monitor the distribution of PGs, of which many map PGs based on spectral responses and geometric shapes. In this study, we proposed a new fine- and coarse-scale mapping approach using two-temporal Sentinel-2 images with various seasonal characteristics and a one-dimensional convolutional neural network (1D-CNN). Having applied this approach in a pilot area study, the results were summarized as follows: (1) A time-series analysis of Sentinel-2 images showed that the reflectance of greenhouses changes during crop growth and development. In particular, the red-edge and near-infrared bands undergo a significant increase and then decrease during the whole crop growth period. Thus, two critical period images, containing a substantial difference in greenhouse reflectance, were sufficient to carry out an accurate and efficient mapping result. (2) The 1D-CNN classifier was used to map greenhouses by capturing subtle details and the overall trend of the spectrum curve. Overall, our approach showed higher classification accuracy than other approaches using support vector machines (SVM) or random forests (RF). In addition, the greenhouse area identified was highly consistent with the existing surfaces observed in very high-resolution images, with a kappa co-efficient of 0.81. (3) The narrow band feature differences (red-edge and near infrared narrow bands) in two-temporal Sentinel-2 images played a significant role in high-precision greenhouse mapping. The classification accuracy with narrow band features was much better than the maps produced without narrow band features. This scheme provided a method to digitize greenhouse precisely and publish its statistics for free, which enable advanced decision support for agriculture management.

A particle-cluster treecode based on barycentric Hermite interpolation is presented for fast summation of electrostatic particle interactions in 3D. The interpolation nodes are Chebyshev points of the 2nd kind in each cluster. It is noted that barycentric Hermite interpolation is scale-invariant in a certain sense that promotes the treecode’s efficiency. Numerical results for the Coulomb and screened Coulomb potentials show that the treecode run time scales like O(N log N), where N is the number of particles in the system. The advantage of the barycentric Hermite treecode is demonstrated in comparison with treecodes based on Taylor approximation and barycentric Lagrange interpolation.

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Computer has become an indispensable tool in people’s daily production, life and work today. When it is widely used to improve the quality of life and production efficiency, the speed of computer in data processing and operation is widely concerned. In this paper, the author gives a brief introduction to the computer data processing, discusses the demarcation of the computer data processing process, and analyzes several main factors that affect the speed of data processing.