No abstract

No abstract

In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses a hierarchical framework that is divided into a 4D cooperative planning layer and a local threat avoidance planning layer. In the cooperative planning layer, the proposed algorithm, named dynamic priority rapidly exploring random trees (DPRRT*), would be used for the 4D cooperative path planning of all UAVs involved in a given task. We first designed a heuristic prioritization strategy in the DPRRT* algorithm to rank all UAVs to improve the efficiency of cooperative planning. Then, the improved RRT* algorithm with the 4D coordination cost function was used to plan the 4D coordination path for each UAV. Whenever the environment changes dynamically (i.e., sudden static or moving threats), the proposed heuristic artificial potential field algorithm (HAPF) in the local threat avoidance planning layer is used to plan the local collision avoidance path. After completing local obstacle avoidance planning, the DPRRT* of the 4D cooperative planning layer is again called upon for path replanning to finally realize 4D cooperative path planning for all UAVs. The simulation and comparison experiments prove the feasibility, efficiency, and robustness of the proposed algorithm.

Background Throid cancer is one of the most common cancer worldwide and its mechanism of development remains elusive.Apolipoprotein M (ApoM) is associated with lipid metabolism, inflammation and atherosclerosis, but the prognostic value of apoM in thyroid cancer has not been well studied.Methods In this study, transcriptional expression, survival, gene ontology and networks of apoM in patients with thyroid cancer were analyzed using integrated bioinformatics tools including UALCAN, GEO, LinkedOmics, GeneMANIA, STRING, CircNET and KOBAS.Results Results indicated that ApoM is decreased in thyroid cancer tissues and is therefore negatively associated with malignant clinicopathological parameters. However, Kaplan-Meier analyses showed that ApoM expression is not an independent and significant prognostic factor for overall survival in thyroid cancer. LinkedOmicsConclusions These results indicate that integrated bioinformatics analysis provide valuable information on apoM expression and potential regulatory networks in thyroid cancer. This information will be crucial in understanding the role of apoM in thyroid carcinogenesis.