A Deep Learning Approach for Wireless Network Performance Classification Based on UAV Mobility Features

The unmanned aerial vehicle (UAV) has drawn attention from the military and researchers worldwide, which has advantages such as robust survivability and execution ability. Mobility models are usually used to describe the movement of nodes in drone networks. Different mobility models have been proposed for different application scenarios; currently, there is no unified mobility model that can be adapted to all scenarios. The mobility of nodes is an essential characteristic of mobile ad hoc networks (MANETs), and the motion state of nodes significantly impacts the network’s performance. Currently, most related studies focus on the establishment of mathematical models that describe the motion and connectivity characteristics of the mobility models with limited universality. In this study, we use a backpropagation neural network (BPNN) to explore the relationship between the motion characteristics of mobile nodes and the performance of routing protocols. The neural network is trained by extracting five indicators that describe the relationship between nodes and the global features of nodes. Our model shows good performance and accuracy of classification on new datasets with different motion features, verifying the correctness of the proposed idea, which can help the selection of mobility models and routing protocols in different application scenarios having the ability to avoid repeated experiments to obtain relevant network performance. This will help in the selection of mobility models for drone networks and the setting and optimization of routing protocols in future practical application scenarios

Uridine modulates monoclonal antibody charge heterogeneity in Chinese hamster ovary cell fed-batch cultures

Abstract Background Charge heterogeneity is one of the most critical quality attributes of antibodies, which has strong influence on drug’s biological activity and safety. Finding out the key components that affecting charge variants is of great significance for establishing a competitive culture process. In this study, we first illustrated uridine’s great impacts on antibody charge heterogeneity in CHO cell fed-batch cultures. Results Uridine was beneficial to cell growth and the maintenance of cell viability, which made IVCC increased by 50% and the final titer improved by 64%. However, uridine had great influences on mAb’s charge variants. In uridine added cultures, the acidic variant levels were about 9% lower than those in control cultures, while the basic variant levels were about 6% higher than those in control cultures. Further investigation found that the decrease of aggregates and glycated forms were responsible for the reduction of acidic variants. What’s more, uridine decreased the lysine variant levels. Conclusions Uridine’s addition to fed-batch promoted cell growth and the final titer, in the meanwhile, uridine decreased the acidic variants dramatically. Therefore, feeding uridine is an efficient way to control the generation of acidic charge variants in up-stream process. These findings provide new ideas and guidance for the control and optimization of antibody charge heterogeneity in culture process developments