With the advent of 5G communication standards, the number of 5G base stations increases steadily, and the number of mobile terminals and IoT (Internet of Things) devices increases sharply, which sharps a large number of IoT devices and forms a complex network. These devices can take as nodes of a community in the opportunistic social network. However, in the environment of traditional opportunistic network algorithm and mass data transmission, information transmission is only carried out at several source nodes in the community, which usually leads to transmission delay, excessive energy consumption, and source node death. Therefore, we propose an effective data delivery based on the multiperceived domain algorithm, which recombines communities based on the correlation degree of nodes, and new communities assist source nodes to transmit information in solving these problems. The comparison between the experiment and the classical opportunistic network algorithm shows that the method has outstanding performance in reducing the resource consumption of data transmission and improving the efficiency of information transmission.

In opportunistic complex networks, information transmission between nodes is inevitable through broadcast. The purpose of broadcasting is to distribute data from source nodes to all nodes in the network. In opportunistic complex networks, it is mainly used for routing discovery and releasing important notifications. However, when a large number of nodes in the opportunistic complex networks are transmitting information at the same time, signal interference will inevitably occur. Therefore, we propose a low-latency broadcast algorithm for opportunistic complex networks based on successive interference cancellation techniques to improve propagation delay. With this kind of algorithm, when the social network is broadcasting, this algorithm analyzes whether the conditions for successive interference cancellation are satisfied between the broadcast links on the assigned transmission time slice. If the conditions are met, they are scheduled at the same time slice, and interference avoidance scheduling is performed when conditions are not met. Through comparison experiments with other classic algorithms of opportunistic complex networks, this method has outstanding performance in reducing energy consumption and improving information transmission efficiency.

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Basic fibroblast growth factor (bFGF) and platelet-derived growth factor (PDGF) have been shown to be involved in a spectrum of cellular processes. In a previous study, we constructed a novel multigenic vector that contained two separate transcription units, each consisting of a strong promoter and an efficient polyadenylation signal. The two promoters were chosen for their ability to work simultaneously. Dual gene transfer of bFGF and PDGF in a single plasmid resulted in a significant increase in collateral blood vessel formation in a rabbit model of hind limb ischemia. The aim of the present study was to investigate the effect of this dual gene transfer strategy in a rat model of acute myocardial infarction (AMI). AMI was induced in rats by ligation of the left anterior descending coronary artery. The animals were randomly divided into four groups and treated with the following therapeutic strategies: Empty plasmid (control), plasmid encoding bFGF (PL-bFGF), plasmid encoding PDGF (PL-PDGF) or plasmid encoding bFGF and PDGF (PL-F-P). Echocardiography and histological examinations were performed 28 days subsequent to gene transfer. Dual gene therapy with bFGF and PDGF resulted in a significant angiogenic effect accompanied by vessel maturation, along with a significant reduction in infarct size and improvement in cardiac function. In a rat model of AMI, single plasmid-mediated dual gene therapy with bFGF and PDGF decreased infarct size and improved cardiac function due to the formation of functionally and morphologically mature vasculature. These results are relevant to the ongoing clinical trials involving the use of single plasmid-mediated angiogenic factors for the treatment of myocardial ischemic disease.

With the rapid popularization of 5G communication and internet of things technologies, the amount of information has increased significantly in opportunistic social networks, and the types of messages have become more and more complex. More and more mobile devices join the network as nodes, making the network scale increase sharply, and the tremendous amount of datatransmission brings a more significant burden to the network. Traditional opportunistic social network routing algorithms lack effective message copy management and relay node selection methods, which will cause problems such as high network delay and insufficient cache space. Thus, we propose an opportunistic social network routing algorithm based on user-adaptive data transmission. The algorithm will combine the similarity factor, communication factor, and transmission factor of the nodes in the opportunistic social network and use information entropy theory to adaptively assign the weights of decision feature attributes in response to network changes. Also, edge nodes are effectively used, and the nodes are divided into multiple communities to reconstruct the community structure. The simulation results show that the algorithm demonstrates good performance in improving the information transmission’s success rate, reducing network delay, and caching overhead.

With the popularization of 5G communications, the scale of social networks has grown rapidly, and the types of messages have become increasingly complex. The rapid increases in the number of access nodes and the amount of data have put a greater burden on the transmission of information in the networks. However, when transferring data from a large number of users, the performance of traditional opportunistic network routing algorithms is insufficient, which often leads to problems such as high energy consumption, network congestion, and data packet loss. The way in which to improve this transmission environment has become a difficult task. Therefore, in order to ensure the effective transmission of data and reduce network congestion, this paper proposed a link prediction model based on triangular relationships in opportunistic social networks (LPMBT). In the topological structure of the social network, the algorithm scores links based on the frequency of use and selects the optimal relay node based on the score. It can also efficiently track the target node and reconstruct the sub-community. The simulation experimental results showed that the algorithm had excellent performance, effectively reduced overhead, reduced the end-to-end delay, and greatly improved the data transfer rate in the opportunistic network.

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