In this paper, a novel artificial neural network (ANN)‐based nonlinear output current model with accurate high‐order derivatives is presented. The proposed model can guarantee high precision of the drain current and its high‐order derivatives simultaneously. The existing traditional equation‐based models are analyzed to demonstrate their limits and inaccurate modeling performance of high‐order derivatives. Aiming at maintaining high accuracy of the drain current and its derivatives at the same time, the ANN has been used iteratively. This method, which inherits the advantage of ANN model, obtains higher accuracy than existing ANN model as well as traditional equation‐based models. The effectiveness of the model has been verified by comparing the modeled and measured results for a GaAs pHEMT.

The paper detects the association between carbon performance (CP) and corporate financial performance (CFP) and the moderating role of consumer awareness (CA) of corporate social responsibility (CSR). We find that CP has consistent positive impacts on CFP in the short and long term, whereas CA has opposite moderating effects on CFP. These results indicate that companies should increase corporate value by improving CP. In addition, improving CA of CSR is an effective way to promote CFP.

Because the node energy and network resources in the wireless sensor network (WSN) are very finite, it is necessary to distribute data traffic reasonably and achieve network load balancing. Ad hoc on-demand multipath distance vector (AOMDV) is a widely used routing protocol in WSN, but it has some deficiencies: establishes the route by only using hop counts as the routing criterion without considering other factors such as energy consumption and network load; forwards route request in fixed delay resulting in building the nonoptimal path; and cannot update the path status after built paths. For the deficiency of AOMDV, this paper proposes a multipath routing protocol adaptive energy and queue AOMDV (AEQAOMDV) based on adaptively sensing node residual energy and buffer queue length. When sending a routing request, the forwarding delay of the routing request is adaptively adjusted by both the residual energy and the queue length of the intermediate node; when establishing routes, a fitness is defined as a routing criterion according to the link energy and the queue load, predicting the available duration of the node based on the energy consumption rate and adjusting the weight of the routing criterion by the available duration of the node; after the routes are established, the path information status are updated via periodically broadcasting Hello that carries the path information with the minimum fitness, making the source node update the path information periodically. By using NS-2, simulations demonstrate that compared with AOMDV, AEQAOMDV has obvious improvements in increasing packet delivery ratio, reducing network routing overhead, reducing route discovery frequency, and decreasing the network delay. And AEQAOMDV is more suitable for WSN.

No abstract

No abstract