Search

Aiming at the operational efficiency of small farm machinery groups in cooperative operations in hilly areas, this study proposes a static task allocation model. This method combines the optimal gene fragment retention method based on a genetic algorithm (OGFR-GA) and the method for generating multi-loop weighted connected graphs based on Prim's algorithm (MLW-Prim). The collaborative objective function for the harvester group was established by considering factors such as operation time, fuel consumption, and distance. The OGFR-GA was designed and applied multiple times to obtain several optimal gene fragments corresponding to the number of farm machines. These fragments were used as critical paths in the weighted connected graph generated based on farm machinery performance parameters and task parameters. The MLW-Prim method was proposed to construct this weighted connected graph and realize the static task allocation model for multi-machine cooperative operations. Simulation experiments showed that the model combining OGFR-GA and MLW-Prim achieved optimal values with fewer iterations, and reduced both group cost and cost variance compared to traditional algorithms. This method meets the static task allocation needs for multi-machine cooperative operations in agricultural production and can be integrated with intelligent operations in mountainous and hilly regions, laying a theoretical foundation for improving efficiency. [ABSTRACT FROM AUTHOR]

The maximum load of wing and fuselage increases sharply while flapping-wing micro air vehicles(FWMAVS) flutters at high frequencies, which seriously affect the flight performance and flight life. The mathematical models of wing lift, drag and inertial forces during the FWMAVs fluttering are established, based on the analysis of movement and force conditions of the wing. A multi-objective optimization model targeting at improving the distribution of loads in the time domain is then proposed and solved by using NSGA-Ⅱ algorithm in Matlab environment. After all the work, the Pareto optimal solution set of the hovering state is obtained. Comparison and analysis of the data show that after the optimization, the peaks of lift and inertial forces decline significantly and the load distribution is obviously improved.

The primary objective of precipitation forecasting is to accurately predict short-term precipitation at a high resolution within a specific area, which is a significant and intricate challenge. Traditional models often struggle to capture the multidimensional characteristics of precipitation clouds in both time and space, leading to imprecise predictions due to their expansion, dissipation, and deformation. Recognizing this limitation, we introduce ConvLSTM-UNet, which leverages spatiotemporal feature extraction from meteorological images. ConvLSTM-UNet is an efficient convolutional neural network (CNN) based on the classical UNet architecture, equipped with ConvLSTM and improved deep separable convolutions. We evaluate our approach on the generic time series dataset Moving MNIST and the regional precipitation dataset of the Netherlands. The experimental results show that the proposed method has better spatiotemporal prediction skills than other tested models, and the mean squared error is reduced by more than 7.2%. In addition, the visualization results of the precipitation forecast show that the approach has a better ability to capture heavy precipitation, and the texture details of the precipitation forecast are closer to the ground truth. [ABSTRACT FROM AUTHOR]

Rhizosphere sediments and plant samples were collected at five sites in a muddy tidal flat of Yangtze Estuary. The enzymatic activities and heavy metal concentrations in the sediment-plant system of the muddy tidal flat were analyzed. The results indicated that in muddy tidal plants, Cr and Zn were accumulated in roots and more Mn was transported upwards and accumulated in the plant tissues above the ground. The urease activities, content of Active Mn, Active Zn and Active Cu, and the concentration of Zn in the whole plant-sediment system were influenced by OM and EC in the rhizosphere sediment. Enzymatic activities were sensitive to the heavy metals in sediment-plant system of the muddy tidal flat. Urease activities were inhibited by Active Mn and Active Zn. Catalase activities were greatly activated by Active Cr and Active Mn. Acid phosphatase activities could be activated by Active Cr. Alk-pho had positive correlations with Cr in the plant body ( R = 0.795, p < 0.01). The activities of urease could be used as an index for the evaluation of Active Mn and Active Zn in the rhizosphere sediment. The activities of alkaline phosphatase could be an effective indicator for the phyto-availability of Cr in sediment-plant system of Yangtze Estuary. [ABSTRACT FROM AUTHOR]