Multi-unmanned aerial vehicle (UAV) flight formations can be deployed to monitor large areas. Individual UAVs communicate and exchange information while formation flying. but, such communication presents a security risk. The area between the UAV group range and the group communication range is called the insecurity range and in this region multi-UAV communication can cause serious information leakage. To resolve this problem, this paper considers two aspects, namely, cooperative control and secure communication. To implement cooperative control, a clustering algorithm is presented to accelerate the speed at which the multi-UAV formation converges. By setting the flight control factor to accelerate the convergence of multi-UAV, the UAV group forms a flock. To facilitate secure communication, the hierarchical virtual communication ring (HVCR) strategy is deployed to reduce the boundary of group communication and minimize the insecure range. The effectiveness of the clustering algorithm and HVCR strategy is demonstrated via theoretical analysis and experiments. In the case of 50 and 100 nodes, the results show that the clustering algorithm can facilitate multi-UAV group flocks. In the case of 25, 30, 35 and 40 nodes, the HVCR strategy can reduce the relative size of the insecure range to 65.33%, 62.95%, 61.50% and 60.55%, respectively.

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Hemorrhagic stroke is one of the most devastating diseases worldwide due to a high rate of disability and mortality with few effective treatments. Recent advances in nanomedicines to promote hemostasis, drug delivery, neuroprotection, and nerve regeneration may provide insight into hemorrhagic stroke treatment. In this review, we first view the pathophysiology and conventional therapeutics of hemorrhagic stroke. Second, we comprehensively summarize the current nanomedicines applied in hemorrhagic stroke, including inorganic nanomaterials, polymer-based nanomaterials, lipid-based nanomaterials, self-assembling peptide-based hydrogel, exosomes, and gel systems. Finally, the challenges, opportunities, and future perspectives of nanomedicines for hemorrhagic stroke are discussed. Thus, this review promotes greater exploration of effective therapies for hemorrhagic stroke with nanomedicines.

Energy optimization is a critical issue in three-dimensional (3D) underwater acoustic sensor networks (UASNs). Intelligent path planning can be applied to extend the lifetime of autonomous underwater vehicles (AUVs) which has attracted many researchers' attention as a key component of UASNs in recent years. In this paper, we put forward an algorithm of distance evolution nonlinear particle swarm optimization (DENPSO), aiming at finding an energy-efficient stable path for AUVs in 3D UASNs. First, in order to ensure that the particles fully explore the 3D underwater environment during the evolution process, we convert the inertia weighting factor and learning factor from linearity to nonlinearity. Second, to avoid particles falling into local optimum regions, the particles of the poor search regions are randomly perturbed by the distance evolution factor. Third, we apply the penalty function to describe the energy optimization goal under the obstacles and ocean currents. To quantify the role of obstacle avoidance in the penalty function, each path is divided into several micro-element points based on the cubic spline interpolation method. Then, we propose a degree value factor to measure the micro-element points falling within the obstacle coverage regions. Finally, simulations are finished in 3D underwater environment and the real environment based on regional ocean model system (ROMS). The results show that DENPSO can avoid the obstacles along the eddy current direction, where the energy consumption of algorithm DENPSO is, respectively, reduced by 2.1514e+03 J and 1.049e+07 J compared with the algorithm LPSO in the above-mentioned environment.

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Aiming at the secure communication problems between multi-unmanned aerial vehicles (multi-UAVs) caused by wireless signal strength increasing, a wireless communication model and UAV dynamic model is established in this paper. The UAV communication range is further narrowed by the application of improved artificial fish-swarm algorithm (IAFSA). By taking dynamic parameters as adaptive vision's coefficient and introducing virtual ocean currents into the artificial fish algorithm (AFSA), the drawback of slow convergence speed and more iteration has been made up by the IAFSA. It is proved by the simulation experiments that compared to AFSA, IAFSA has the advantage of fast convergence rate and less iteration when solving the problem of multi-UAVs secure communication. Additionally, the overall communication range has also been narrowed by the IAFSA.

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