Microplastic (MP) pollution in agricultural ecosystems is an emerging environmental concern, with limited knowledge of its transport and accumulation in rural waterbodies. This study investigates the distribution and sources of MP in drainage ditches influenced by pond connectivity, land use, and soil properties within a small catchment in Nanjing, East China. Sediment was collected from ditches in 18 sites across forest, agricultural, horticultural, and urban areas. Using laser-directed infrared spectroscopy (LDIR), 922 MP particles were identified. Six materials were dominant: fluororubber (FR), polyethylene terephthalate (PET), polyurethane (PU), acrylonitrile (ACR), chlorinated polyethylene (CPE), and polyethylene (PE). MP concentrations varied by land use and pond connectivity, with ditches above ponds exhibiting higher counts (1700 particles/kg) than those below (1050 particles/kg), indicating that ponds act as MP sinks. The analysis revealed site-specific MP sources, with FR linked to road runoff and PET associated with agricultural practices. Correlations between MP shape and soil properties showed that more compact and filled shapes were more commonly associated with coarser soils. PE particle size was negatively correlated with organic matter. This study highlights the need for targeted strategies to reduce MP pollution in rural landscapes, such as reducing plastic use, ditch maintenance, and improved road runoff management.

Multirobot motion planning is always one of the critical techniques in edge intelligent systems, which involve a variety of algorithms, such as map modeling, path search, and trajectory optimization and smoothing. To overcome the slow running speed and imbalance of energy consumption, a swarm intelligence solution based on parallel computing is proposed to plan motion paths for multirobot with many task nodes in a complex scene that have multiple irregularly‐shaped obstacles, which objective is to find a smooth trajectory under the constraints of the shortest total distance and the energy‐balanced consumption for all robots to travel between nodes. In a practical scenario, the imbalance of task allocation will inevitably lead to some robots stopping on the way. Thus, we firstly model a gridded scene as a weighted MTSP (multitraveling salesman problem) in which the weights are the energies of obstacle constraints and path length. Then, a hybridization of particle swarm and ant colony optimization (GPSO‐AC) based on a platform of Compute Unified Device Architecture (CUDA) is presented to find the optimal path for the weighted MTSPs. Next, we improve the A∗ algorithm to generate a weighted obstacle avoidance path on the gridded map, but there are still many sharp turns on it. Therefore, an improved smooth grid path algorithm is proposed by integrating the dynamic constraints in this paper to optimize the trajectory smoothly, to be more in line with the law of robot motion, which can more realistically simulate the multirobot in a real scene. Finally, experimental comparisons with other methods on the designed platform of GPUs demonstrate the applicability of the proposed algorithm in different scenarios, and our method strikes a good balance between energy consumption and optimality, with significantly faster and better performance than other considered approaches, and the effects of the adjustment coefficient q on the performance of the algorithm are also discussed in the experiments.

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Recently, a simultaneous cosparsity and low-rank (SCLR) optimization model has shown the state-of-the-art performance in compressive sensing (CS) recovery of multichannel EEG signals. How to solve the resulting regularization problem, involving l0 norm and rank function which is known as an NP-hard problem, is critical to the recovery results. SCLR takes use of l1 norm and nuclear norm as a convex surrogate function for l0 norm and rank function. However, l1 norm and nuclear norm cannot well approximate the l0 norm and rank because there exist irreparable gaps between them. In this paper, an optimization model with lq norm and schatten-p norm is proposed to enforce cosparsity and low-rank property in the reconstructed multichannel EEG signals. An efficient iterative scheme is used to solve the resulting nonconvex optimization problem. Experimental results have demonstrated that the proposed algorithm can significantly outperform existing state-of-the-art CS methods for compressive sensing of multichannel EEG channels.