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Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining free, moving, growing, and solid states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model’s accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.

The atypical Markov decision processes (MDPs) are decision-making for maximizing the immediate returns in only one state transition. Many complex dynamic problems can be regarded as the atypical MDPs, e.g., football trajectory control, approximations of the compound Poincaré maps, and parameter identification. However, existing deep reinforcement learning (RL) algorithms are designed to maximize long-term returns, causing a waste of computing resources when applied in the atypical MDPs. These existing algorithms are also limited by the estimation error of the value function, leading to a poor policy. To solve such limitations, this paper proposes an immediate-return algorithm for the atypical MDPs with continuous action space by designing an unbiased and low variance target Q-value and a simplified network framework. Then, two examples of atypical MDPs considering the uncertainty are presented to illustrate the performance of the proposed algorithm, i.e., passing the football to a moving player and chipping the football over the human wall. Compared with the existing deep RL algorithms, such as deep deterministic policy gradient and proximal policy optimization, the proposed algorithm shows significant advantages in learning efficiency, the effective rate of control, and computing resource usage.

The integrated fast detection technology for electric bikes, riders, helmets, and license plates is of great significance for maintaining traffic safety. YOLOv5 is one of the most advanced single-stage object detection algorithms. However, it is difficult to deploy on embedded systems, such as unmanned aerial vehicles (UAV), with limited memory and computing resources because of high computational load and high memory requirements. In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5. Firstly, the YOLOv5s backbone network and the Neck part are lightened by combining the two lightweight networks, ShuffleNetv2 and GhostNet, included. Secondly, by adopting an Add-based feature fusion method, the number of parameters and the floating-point operations (FLOPs) are effectively reduced. On this basis, a scene-based non-truth suppression method is proposed to eliminate the interference of pedestrian heads and license plates on parked vehicles, and then the license plates of the riders without helmets can be located through the inclusion relation of the target boxes and can be extracted. To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate. The results show that, the SG-YOLOv5 has the same mean average precision (mAP0.5) as the original; the number of model parameters, the FLOPs, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively. Additionally, the number of frames per second (FPS) is 2.7 times higher than that of the original. Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy.

Since the full implementation of the ‘one-shoulder pole’ policy in 2019, village secretaries are now expected to concurrently serve as village heads. Simultaneously, the question arises as to how village secretaries can be guaranteed to be elected as village heads. Drawing on an in-depth case study of Y County, this paper shows that the local government has adopted an approach I call ‘first democracy, then centralism’ to ensure that village elections involve democratic elements, such as open nomination and candidate competition, yet also produce a designated winner who is, in the government’s view, most qualified to govern the village. Such an approach has generated two consequences on village elections: First, the significance of village Party elections has greatly increased. Since the result of village committee elections is already certain, Party elections become the key phase that determines who will hold village office and in which candidates still have an opportunity to compete against one another. Second, it is now imperative for candidates to earn support and approval from the local government in order to become the village leaders in the new era. [ABSTRACT FROM AUTHOR]

Hexavalent chromium, namely, Cr(VI), is a significant threat to ecological and food safety. Current detection methods are not sensitive to Cr(VI). A photoelectrochemical (PEC) sensor based on bismuth vanadate (BiVO4) was developed for sensitive detection of Cr(VI). First, BiVO4-X (X: the pH of the reaction precursor solution) was synthesized using a facile surfactant-free hydrothermal method. The BiVO4-X morphology was well controlled according to pH values, showing rock-like (X = 1), wrinkled bark-like (X = 4), carnation-like (X = 7), and the collapsed sheet-like morphologies (X = 9, 12). BiVO4-7 exhibited excellent photoelectric performance due to a proper band structure under visible light and a large specific surface area. Then, BiVO4-7 was used to construct a PEC sensor to detect Cr(VI), which was demonstrated to have a low detection limit (10 nM) and wide detection range (2–210 μM). The BiVO4-7 PEC sensor had a stable output signal, as well as excellent reproducibility, repeatability, and selectivity. We used the BiVO4-7 PEC sensor to detect Cr(VI) in real environmental and food samples, resulting in a satisfactory recovery of 90.3–103.0%, as determined by comparison with results obtained using a spectrophotometric method. The BiVO4-7 PEC sensor is promising for practical application to heavy metal detection in the food and environment.