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Dense trajectory has become one of the most successful hand-crafted features for action recognition. However, most of the existing dense trajectories based methods ignore the relationship between trajectories. In this paper, we propose multiple relative descriptors of trajectories to model the relative information of pairs of trajectories. Specifically, we present relative motion descriptors and relative location descriptors, which are utilized to capture the relative motion information and relative location information respectively. Moreover, we present relative deep feature descriptors which combine the deep features with hand-crafted features. By aggregating the above descriptors, we obtain the fixed-length representation regardless of the various duration of input video. The experimental results on three standard datasets demonstrate the superiority of our method.

The deep convolution neural network has shown great potential in the field of human action recognition. For the sake of obtaining compact and discriminative feature representation, this paper proposes multiple pooling strategies using CNN features. We explore three different pooling strategies, which are called space-time feature pooling (STFP), time filter pooling (TFP) and spatio-temporal pyramid pooling (STPP), respectively. STFP shares the advantages of both hand-crafted features and deep ConvNets features. TFP reflects the change of elements on each CNN feature map over time. STPP focuses on the spatial and temporal pyramid structure of the feature maps. We aggregate these pooled features to produce a new discriminative video descriptor. Experimental results show that the three strategies have complementary advantages on the challenging YouTube, UCF50 and UCF101 datasets, and our video representation is comparable to the previous state-of-the-art algorithms.

Based on the idea of data-driven control, a novel iterative adaptive dynamic programming (ADP) algorithm based on the globalized dual heuristic programming (GDHP) technique is used to solve the optimal control problem of discrete-time nonlinear switched systems. In order to solve the Hamilton–Jacobi–Bellman (HJB) equation of switched systems, the iterative ADP method is proposed and the strict convergence analysis is also provided. Three neural networks are constructed to implement the iterative ADP algorithm, where a novel model network is designed to identify the system dynamics, a critic network is used to approximate the cost function and its partial derivatives, and an action network is provided to obtain the approximate optimal control law. Two simulation examples are described to illustrate the effectiveness of the proposed method by comparing with the heuristic dynamic programming (HDP) and dual heuristic programming (DHP) methods.

Soil microorganisms are central to nutrient cycling and soil fertility, and their dynamics are strongly influenced by agricultural management practices. Peanut shell biochar has been widely applied to enhance soil fertility and reduce nutrient loss. However, its long-term effects on soil microbial communities under large-scale field conditions remain poorly understood. To address this knowledge gap, we conducted multi-year field experiments across five major tobacco-growing regions in China. Compared with the control group, long-term addition of peanut shell biochar significantly improved various soil chemical properties in Mudanjiang, Shangluo, Yichun, and Yanshan Town, including pH, available potassium, available phosphorus, organic matter, carbon-to-nitrogen ratio, alkaline hydrolyzable nitrogen, sucrase activity, catalase activity, and urease activity, while reducing the available phosphorus and catalase activity in Xuchang. Soil microbial diversity and community composition exhibited significant variation across sites, primarily shaped by differences in soil chemical properties. Although overall microbial diversity was not significantly altered by biochar addition, specific taxa, such as Firmicutes, Zoopagomycota, and Blastocladiomycota, were enriched, with Bacilli representing 70% of the significantly enriched bacterial taxa. Co-occurrence network analysis revealed that biochar amendment enhanced the complexity and stability of bacterial networks but reduced those of fungal networks. Furthermore, long-term biochar application enhanced soluble sugar content through pathways involving soil organic matter, bacterial community diversity, and specific enriched bacterial taxa. Collectively, these findings underscore the important role of peanut shell biochar in promoting the stability of bacterial networks and enhancing crop quality, providing a sustainable strategy for improving soil health and agricultural productivity. Graphical Abstract Highlights Peanut shell biochar (PSB) improved soil nutrients and enzyme activities across sites. The microbial diversity and composition of rhizosphere soil were significantly influenced by sites and driven by soil chemistry. The relative abundance of Blastocladiomycota, Zoopagomycota, and Firmicutes was significantly increased by PSB, especially the class Bacilli. The complexity and stability of the rhizosphere soil bacterial networks were increased by PSB. PSB indirectly enhanced tobacco leaf soluble sugar content by altering soil organic matter and bacterial communities.