The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.
In this article, the authors present a new novel energy-efficient and fault-tolerant evolution model for large-scale wireless sensor networks based on complex network theory. In the evolution model, not only is the residual energy of each node considered, but also the constraint of links is introduced, which makes the energy consumption of the whole network more balanced. Furthermore, both preferential attachment and random attachment to the evolution model are introduced, which reduces the proportion of the nodes with high degree while keeping scale-free network characteristics to some extent. Theoretical analysis shows that the new model is an extension of the BA model, which is a mixed model between a BA model and a stochastic model. Simulation results show that EFEM has better stochastic network characteristics while keeping scale-free network characteristics if the value of random probability is near 0.2 and it can help to construct a high survivability network for large-scale WSNs.
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