N concentration of old leaves and twigs is more sensitive to stand density than that of young ones in Chinese fir plantations： a case study in subtropical China

Young leaves are conventionally used in the analysis to study the nutrient status of evergreen plants and their responses to environmental changes, but the role of old leaves remains poorly understood. We selected two stand types in 31-year-old Chinese fir（Cunninghamia lanceolata） plantations with similar soil conditions but different stand densities, to test the hypothesis that nitrogen（N） concentration of old leaves and twigs is more sensitive to stand density than that of young ones. Leaves and twigs were sampled and sorted into young（one-year-old） and old（two-and three-year-old） groups. Significant differences in N concentration and carbon： nitrogen ratio between the low-density stand and high-density stand were only found in the old leaves and twigs but not in the young ones.Although the N resorption efficiency did not vary significantly with stand density, the annual N resorption rates were increased in old leaves and relatively young twigs at high stand density. These results show the potential use of old tissues in the nutrient analysis in Chinese fir plantations. Testing the generality of these results could improve the use of foliar analysis as an indicator of nutrient status and environmental changes in evergreen tree species.

Accumulation of residual soil microbial carbon in Chinese fir plantation soils after nitrogen and phosphorus additions

Nitrogen （N） and phosphorus （P） additions can affect soil microbial carbon （C） accumulation. However, the mechanisms that drive the changes in residual microbial C that occur after N and P additions have not been well-defined for Chinese fir plantations in subtropical China. We set up six different treatments, viz. a control （CK）, two N treatments （NI： 50kgha-1 a-1; N2： 100 kg ha-1 a-1）, one P treatment （P： 50 kg ha-1 a-1）, and two combined N and P treatments （NIP： 50kgha-1a-1 of N ＋50kgha-1a-1 of P; N2P：100 kg ha-1 a-1 of N ＋ 50 kg ha-1 a-1 of P）. We then investigated the influences of N and P additions on residual microbial C. The results showed that soil pH and microbial biomass decreased after N additions, while microbial biomass increased after P additions. Soil organic carbon （SOC） and residual microbial C contents increased in the N and P treatments but not in the control. Residual microbial C accumulation varied according to treatment and declined in the order： N2P 〉 N1P 〉 N2 〉 N1 〉 P 〉 CK. Residual microbial C contents were positively correlated with available N, P, and SOC contents, but were negatively correlated with soil pH. The ratio of residual fungal C to residual bacterial C increased under P additions, but declined under combined N1P additions. The ratio of residual microbial C to SOC increased from 11 to 14% under the N1P and N2P treatments, respectively. Our results suggest that the concentrations of residual microbial C and the stability of SOC would increase under combined applications of N and P fertilizers in subtropical Chinese fir plantation soils.

Boundedness of Commutators with Lipschitz Functions in Non-homogeneous Spaces

Under the assumption that the underlying measure is a non-negative Radon measure which only satisfies some growth condition, the authors prove that for a class of commutators with Lipschitz functions which include commutators generated by Calderon-Zygrnund operators and Lipschitz functions as examples, their boundedness in Lebesgue spaces or the Hardy space H^1 （μ） is equivalent to some endpoint estimates satisfied by them. This result is new even when the underlying measure μ is the d-dimensional Lebesgue measure.

MDvT: introducing mobile three-dimensional convolution to a vision transformer for hyperspectral image classification

Hyperspectral images carry numerous spectral bands,and their wealth of band data is a valuable source of information for the accurate classification of ground objects.Three-dimensional(3D)convolution,although an excellent spectral information extraction method,is limited by its huge number of parameters and long model training time.To allow better integration of 3D convolution with the most popular transformer models currently available,a new architecture called mobile 3D convolutional vision transformer(MDvT)is proposed.The MDvT introduces inverted residual structure to reduce the number of model parameters and balance the data mining efficiency of low-dimensional data input.Simultaneously,a square patch is used to cut the sequence of tokens to accelerate the model operation.Through extensive experiments,we evaluated the classification overall performance of the proposed MDvT on the WHU-Hi and Pavia University datasets,and demonstrated significant improvements in classification accuracy and model runtime compared with classical deep learning models.It is worth noting that compared with directly integrating 3D convolution into the transformer model,the MDvT architecture improves the accuracy while reducing the time to train an epoch by approximately 58.54%.To facilitate the reproduction of the work in this paper,the model code is available at https://github.com/gloryofroad/MDvT.