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With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms.

A Petri net based framework is proposed for automatic high level video event description, recognition and reasoning purposes. In comparison with the existing approaches reported in the literature, our work is characterized with a number of novel features: (i) the high level video event modeling and recognition based on Petri net are fully automatic, which are not only capable of covering single video events but also multiple ones without limit; (ii) more variations of event paths can be found and modeled using the proposed algorithms; (iii) the recognition results are more accurate based on automatic built high level event models. Experimental results show that the proposed method outperforms the existing benchmark in terms of recognition precision and recall. Additional advantages can be achieved such that hidden variations of events hardly identified by humans can also be recognized.

Carbapenem-resistant Enterobacteriaceae have been a global public health issue in recent years. Here, a carbapenem-resistant Kluyvera cryocrescens strain SCW13 was isolated from hospital sewage, and was then subjected to whole-genome sequencing (WGS). Based on WGS data, antimicrobial resistance genes were identified. Resistance plasmids were completely circularized and further bioinformatics analyses of plasmids were performed. A conjugation assay was performed to identify a self-transmissible plasmid mediating carbapenem resistance. A phylogenetic tree was constructed based on the core genome of publicly available Kluyvera strains. The isolate SCW13 exhibited resistance to cephalosporin and carbapenem. blaNDM-1 was found to be located on a ~53-kb self-transmissible IncX3 plasmid, which exhibited high similarity to the previously reported pNDM-HN380, which is an epidemic blaNDM-1-carrying IncX3 plasmid. Further, we found that SCW13 contained a chromosomal blaKLUC-2 gene, which was the probable origin of the plasmid-born blaKLUC-2 found in Enterobacter cloacae. Phylogenetic analysis showed that K. cryocrescens SCW13 exhibited a close relationship with K. cryocrescens NCTC10483. These findings highlight the further dissemination of blaNDM through clonal IncX3 plasmids related to pNDM-HN380 among uncommon Enterobacteriaceae strains, including Kluyvera in this case.