In post-disaster recovery scenarios, Unmanned Aerial Vehicles (UAVs) are widely deployed as aerial base stations to ensure deterministic communication services for time-sensitive (TS) flows requiring strict latency and reliability requirements. However, dynamic network topologies may render existing schedules invalid, and newly arriving flows can cause time-slot conflicts with ongoing transmissions, resulting in low resource utilization. To address these challenges, we propose a Depth-First Search-based Proximal Policy Optimization (DFS-PPO) approach for robust data flow routing and scheduling. The proposed method aims to balance link loads while satisfying reliability and latency constraints. A pruned DFS algorithm is used to determine the candidate routing set. To capture the spatial dependencies of all link states in the network, a Graph Attention Network (GAT) is employed to extract the global network state. In addition, a Transformer is adopted to model the nonlinear relationship between candidate routes and the network state. A PPO agent then makes routing decisions based on the high-level representation generated by the Transformer, enabling the model to adapt dynamically to changing network conditions and effectively mitigate bottlenecks. Simulation results demonstrate that the proposed DFS-PPO approach significantly outperforms benchmark algorithms in terms of network load balancing, reliability, and the number of successfully scheduled TS flows.

With the development of wireless communications, the 6G network is evolving toward dynamics, complexity, and integration. The mobility of nodes and intermittently of links lead to frequent variations in the network topology. When constructing the topology model, the reliability of topology is not only affected by the physical properties of wireless links but also related to the evolution process of nodes and links states, which is indispensable for improving the accuracy of the topology model. In this paper, we propose the evolution model based on dynamic bond percolation to characterize the reliable topology evolution. Firstly, key factors that cause the network topology changes are analyzed, integrating the characteristics of the node mobility and link channel conditions. Especially signal interference, buffer of nodes, and link availability are modeled for wireless link states. Then, the interactions between adjacent links are formulated by an extended Dynamic Bond Percolation (DBP) model to obtain the topology state transition matrix, which can accurately depict the change of link connection. Based on the quantitative analysis of wireless link states, Markov chain and master equation are employed to build the Dynamic Topology Evolution (DTE) model. Meanwhile, the network topology prediction problem is transformed into a linear system solution problem to obtain the steady-state network topology based on the DTE algorithm. Finally, the results suggest that utilizing the DTE model can significantly improve the accuracy of topology prediction and overall network performance.

This study utilized electronic medical record data of RA inpatients to ascertain potential risk factors associated with patient readmission. Specifically, the investigation concentrated on the impact of various Traditional Chinese Medicine (TCM) syndromes, comorbidities, interactions between TCM syndromes and comorbidities, as well as interactions among comorbidities themselves, on the likelihood of hospital readmission among RA patients. The present study employed logistic regression analysis to examine the interaction between dichotomous variables. The findings revealed that individuals diagnosed with rheumatoid arthritis (RA) and experiencing arthralgia caused by wind-cold-dampness, in addition to hyperlipidemia, exhibited a heightened likelihood of hospital readmission. Similarly, RA patients with hyperlipidemia and the syndrome of dampness-heat blocking collaterals were found to be at an even greater risk of readmission. Furthermore, individuals with RA and comorbidities such as atherosclerosis and chronic gastritis were identified as being at a high risk of readmission. Lastly, RA patients with hyperlipidemia and fatty liver were also found to have an increased likelihood of being readmitted to the hospital. Hence, it is advisable to closely monitor the disease progression among the aforementioned population and implement essential interventions in clinical practice to mitigate the readmission rate of patients with rheumatoid arthritis (RA), consequently alleviating the disease and economic burdens experienced by patients.

Exploring the genes associated with rheumatoid arthritis (RA) patients with blood-stasis syndrome may shed light on the pathology studies of RA patients subtyped blood-stasis named in the traditional Chinese medicine (TCM) towards precision and personalized medicine. In this study, GWAS (genome-wide association study) and enrichment analysis were deployed on GSE205962 which is a gene expression data set from whole blood samples of RA patients with blood-stasis syndrome. Together with knowledge accumulated on RA, gene profiling results indicate RA-associated pathways, together with primary immunodeficiency, indicate a highly expressed gene set that is associated with RA patients with blood-stasis syndrome. The genes are AIRE, IFNG, IFNB1, IRF5, IL4, HSP90B2P, IL12B, IFNA1, and FLT1. The genes associated with RA patients with blood-stasis syndrome were validated via raw expression data and evidence collected from the literature.

Differentially expressed genes (DEGs) shared among different yet associated pathways on rheumatoid arthritis (RA) may shed light on its pathology study. In this study, DEGs of RA subtyped liver-kidney deficiency (a syndrome in traditional Chinese medicine) were collected from the GSE205962 dataset toward the roles they played among RA-associated pathways. GWAS (genome-wide association study) and enrichment analysis demonstrated that the AP-1 transcription factor-associated genes are the top DEGs shared by the RA-associated pathways. As a result, the AP-1 delivers complex regulations on immune responses e.g. activating T cells via up-regulating JUN, FOS, MAPKs, and NFKB1B while inhibiting IKBKB, LCK, NFKB1, CD3D, et al. The protein networks of DEGs shared in the T cell receptor signaling pathway and Th17 cell differentiation, together with the AP-1-centered network, were proposed.

The Satellite Terrestrial Integrated Network(STIN) has become an indispensable part of the future network. However, the limited resources, long delay communications and highly exposed channels of SGIN are vulnerable to network attacks, which make the access authentication scheme as the first line of defense in network security. Most of the existing access authentication schemes are based on discrete logarithm and large integer factorization problems, which cannot resist quantum attacks, and the number of interactions between entities are too large. Therefore, we propose a lightweight and certificateless anonymous access authentication scheme based on lattice to solve this problem. We introduce Number Theory Research Unit (NTRU) scheme into the key generation process, to improve the utilization rate of equipment resources and ensure the legitimacy of the communication entity. The performance evaluation results demonstrate that our scheme only needs twice satellite-ground interactions to complete mutual authentication.

The software-defined Low Earth Orbit (LEO) satellite network can effectively improve the flexibility of the inter-satellite networking. However, the impact of Inter-Satellite Link (ISL) attributes on the network topology has not been well investigated in existed works, which leads to the loss of topology information and the unreliability of routing path. In this paper, we propose a dynamic routing algorithm based on ISL attributes to improve the adaptability and reliability of routing in LEO satellite network. Firstly, we construct the utility function of ISL attributes to quantify the link utility. Then, the maximum deviation algorithm is introduced to adaptively calculate the weight of attribute parameters in link utility function, which can quantify the impact of each attribute on link quality. Finally, the mathematical model of path utility is established, the routing path with highest path utility is selected as the optimal routing path. The simulation results show that the proposed scheme can improve the network performance by about 35%, 10% and 20% in terms of packet drop rate, end-to-end delay and throughput.

Loading the functions of base station on satellite can improve the flexibility of user access and expand the effective coverage of Satellite-Terrestrial Integrated Network (STIN). However, the functions of management and control that supports the satellite networking have not been well investigated. A large amount of signaling needs to be forwarded to the ground control center for processing, which increases the delay of network control and management. In this paper, we propose a novel mobile core network architecture that loads the mobility management function of core network on the satellite, which can improve the flexibility of management and control of STIN. Firstly, considering the on-board computing capacity and the dynamic characteristic of satellites, we design the lightweight Satellite Mobile Core Network (SMCN) to improve the flexibility of the Satellite next-generation NodeB (Sat-gNB) networking. Then, the interactive protocol for user access control, mobility control of Sat-gNBs and the coordinated control between ter-restrial network and satellite network are designed to support the mobility management. Finally, in order to optimize the delay of Sat-gNBs networking, we construct the mathematical model for the multi-SMCN deployment. The simulation results show that compared to the architecture of Non-Terrestrial Networks (NTN), the proposed mobile core network architecture can improve the flexibility of mobility management and optimize the network delay.

Acupuncture is an important part of Chinese medicine that has been widely used in the treatment of inflammatory diseases. During the coronavirus disease 2019 (COVID-19) epidemic, acupuncture has been used as a complementary treatment for COVID-19 in China. However, the underlying mechanism of acupuncture treatment of COVID-19 remains unclear. Based on bioinformatics/topology, this paper systematically revealed the multi-target mechanisms of acupuncture therapy for COVID-19 through text mining, bioinformatics, network topology, etc. Two active compounds produced after acupuncture and 180 protein targets were identified. A total of 522 Gene Ontology terms related to acupuncture for COVID-19 were identified, and 61 pathways were screened based on the Kyoto Encyclopedia of Genes and Genomes. Our findings suggested that acupuncture treatment of COVID-19 was associated with suppression of inflammatory stress, improving immunity and regulating nervous system function, including activation of neuroactive ligand-receptor interaction, calcium signaling pathway, cancer pathway, viral carcinogenesis, Staphylococcus aureus infection, etc. The study also found that acupuncture may have additional benefits for COVID-19 patients with cancer, cardiovascular disease and obesity. Our study revealed for the first time the multiple synergistic mechanisms of acupuncture on COVID-19. Acupuncture may play an active role in the treatment of COVID-19 and deserves further promotion and application. These results may help to solve this pressing problem currently facing the world.

Software defined satellite-terrestrial networking has been identified as a promising approach to support the diversity of network services. As the fundamental issue to improve the flexibility of network management, the controller placement problem has been attracted increasing attentions for the integration of satellite and terrestrial networking. However, the impact of the dynamic coverage demands on the controller placement have not been well investigated in existed works, which makes them fail to adjust the coverage dynamically according to the actual demands, and leads to an obvious increase of networking response latency to the terminals. Aiming to address this issue, we propose a novel on-demand dynamic controller placement scheme, which can optimize the placement of controllers to improve networking response latency while meeting the dynamic coverage demands. Firstly, to optimize the number of controllers and meet the dynamic coverage demands, we define the coverage redundancy and propose the redundancy-based satellite subnet division method to establish the reliable satellite subnets. Secondly, we quantify the networking response latency of the distributed satellite subnet, and build an optimization mathematical model to optimize the number and location of controllers. Then, we formulate the controller placement problem into the capacitated facility location problem and build the mathematical model for it. Moreover, the on-demand dynamic approximation algorithm is proposed to obtain the approximation solution. Finally, the simulation results demonstrate that the proposed algorithm can effectively optimize the network latency compared with related algorithms.

Summary The high‐bandwidth demands from variety of applications drive a dense wireless local access network (WLAN), which results in a complicated wireless network scene with serious co‐channel interference and energy waste. In this paper, to reveal the interactions between interference and energy efficiency, we propose an interference‐energy efficiency (IFEE) model to quantify the interference impact on the energy efficiency of 802.11 access point (AP) devices. Firstly, we introduce the channel separation and the difference of received signal strength indication (D‐RSSI) as two indicators to extend the classical signal to interference plus noise ratio (SINR) notion and rate adaptive mechanism. Then, these two parameters are integrated into the energy consumption model to establish the IFEE model. Lastly, we conduct extensive measurements with five typical WiFi interference scene in real network to validate the effectiveness of our model. The comparisons between simulation results and real data demonstrate that the proposed IFEE model can quantify the interference and energy efficiency with high accuracy, which can be used for wireless network optimization and protocol design.