Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.

The low earth orbit (LEO) mega constellation for the internet of thing (IoT) has become one of the hot spots for B5G and 6G concerns. Information-centric networking (ICN) provides a new approach to the interconnection of everything in the LEO mega constellation. In ICN, data objects are independent of location, application, storage and transport methods. Therefore, data naming is one of the fundamental issues of ICN, and research on the data naming mechanism of the LEO mega constellation for the IoT is thus the focus of this study. Adopting a fusion of hierarchical, multicomponent, and hash flat as one structure, a data naming mechanism is proposed, which can meet the needs of the IoT multiservice attributes and high-performance transmission. Additionally, prefix tokens are used to describe hierarchical names with various embedded semantic functions to support multisource content retrieval for in-network functions. To verify the performance of the proposed data naming mechanism, an NS-3-based simulation platform for LEO mega constellations for the IoT is designed and developed. The test simulation results show that, compared with the IP address, the ICN-HMcH naming mechanism can increase throughput by as much as 54% and reduce the transmission delay of the LEO mega satellites for the IoT by 53.97%. The proposed data naming mechanism can provide high quality of service (QoS) transmission performance for the LEO mega constellation for IoT and performs better than IP-based transmission.

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In this paper, we investigate the optimization problem of the transmitter-receiver pairing of spaceborne cluster flight netted radar (SCFNR) for area coverage and target detection. First of all, we propose the novel concept of SCFNR integrated cluster flight spacecraft with netted radar, the mobility model for bistatic radar pair with twin-satellite mode, and formulate the radar-target distance distribution function and radar-target distance product distribution function with geometric probability method. Secondly, by dividing surveillance region into grids, we define the 0-1 grid coverage matrix for bistatic radar and the transmitter-receiver pairing matrix for SCFNR with using radar equation and the radar-target distance distribution function, and we describe the optimal problem of transmitter-receiver pairing of SCFNR for area coverage and target detection by defining K-grid coverage matrix. Thirdly, we propose a new algorithm integrated particle swarm optimization with Hungarian algorithm (PSO-HA) to address the optimal problem, which is actually one-to-one pairing problem. Finally, we validate the effectiveness and reasonability of the proposed algorithm through numerical analysis.

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