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Objective: This study aimed to elucidate the prognostic significance of serum soluble thrombomodulin (sTM), lung ultrasound score (LUS), and lactate levels in patients with extrapulmonary acute respiratory distress syndrome (ARDS), with the goal of refining mortality risk prediction in this cohort.Methods: In a prospective cohort of 95 patients with extrapulmonary ARDS admitted to the intensive care unit, we investigated the primary endpoint of 28-day mortality. Utilizing Lasso-Cox regression analysis, we identified independent prognostic factors for mortality. A predictive nomogram was developed incorporating these factors, and its performance was validated through several statistical measures, including the consistency index, calibration plot, internal validation curve, decision curve analysis, interventions avoided analysis, receiver operating characteristic curve analysis, and Kaplan-Meier survival analysis. We further conducted a subgroup analysis to examine the impact of prone positioning on patient outcomes.Results: The study identified baseline serum sTM, LUS, and lactate levels as independent predictors of 28-day mortality in extrapulmonary ARDS patients. The predictive nomogram demonstrated superior prognostic accuracy compared to the use of sTM, LUS, or lactate levels alone, and outperformed traditional prognostic tools such as the Acute Physiology and Chronic Health Evaluation II score and the partial pressure of arterial oxygen to fractional inspired oxygen ratio. The subgroup analysis did not show a significant impact of prone positioning on the predictive value of the identified biomarkers.Conclusion: Our study results support the development and validation of a novel prognostic nomogram that integrates key clinical biomarkers and ultrasound imaging scores to predict mortality in patients with extrapulmonary ARDS. While our research is preliminary, further studies and validation are required.

To solve the problems of data leakage and identity impersonation caused by traditional single factor authentication, a multi-factor identity authentication scheme based on blockchain was proposed for the trusted sharing of cultural resources.Considering a variety of identity factors, a heterogeneous digital identity model was constructed using the tamper resistance and distribution of the blockchain distributed ledger.Through asymmetric encryption algorithm and exclusive OR (XOR) operation, the trusted reuse of heterogeneous digital identity and fast authentication of multi-agent access were realized.Security analysis and simulation results show that the proposed scheme outperforms existing multi-factor authentication schemes in terms of security and efficiency, and can effectively reduce the cost of identity authentication.

In order to reduce the cost and improve efficiency of power line inspection, UAV (unmanned aerial vehicle), which use mobile edge computing technology to access and process service data, are used to inspect power lines in the energy internet.However, due to the dynamic changes of UAV data transmission demand and geographical location, the edge server load will be unbalanced, which causes higher service processing delay and network energy consumption.Thus, an intelligent inspection task allocation mechanism for energy internet based on deep reinforcement learning was proposed.First, a two-layer edge network task offloading model was established to archive joint optimization of multi-objectives, such as delay and energy consumption.It was designed by comprehensively considering the route of UAV and edge nodes, different demands of services and limited service capabilities of edge nodes.Furthermore, based on Lyapunov optimization theory and dual-time-scaled mechanism, proximal policy optimization algorithm based deep reinforcement learning was used to solve the connection relationship and offloading strategy of edge servers between fixed edge sink layer and mobile edge access layer.The simulation results show that, the proposed mechanism can reduce the service request delay and system energy consumption while ensuring the stability of system.

With the rapid development of Internet of Things (IoT) technology, Network Function Virtualization (NFV) is introduced in the edge network to provide flexible and personalized service. However, there still exist some problems to be solved, such as high cost, unbalanced load, and low availability. Therefore, a reliability-and-energy-balanced Service Function Chain (SFC) mapping and migration method is presented for IoT applications. First, aiming at improving network performance and reducing expenditure, an SFC mapping algorithm based on cost optimization, load balancing, and reliability is proposed to map SFC requests onto the network and provide backup. Second, aiming at optimizing resource configuration, an SFC migration method based on energy consumption and quality of service is proposed to integrate network resources. Simulation results show that the proposed method outperforms the compared algorithms by 15.5% and 24.55% in the acceptance ratio of SFC requests and the overall costs, respectively.

The key indicators affecting business reliability were analyzed in depth,and a network planning model for reliability and construction cost was established.Then an adaptive immune algorithm was designed to solve the network planning model.Then,a multi-level and multi-index oriented evaluation model for network planning method was established,and the weights of the indexes were obtained by using neural network algorithm,so as to achieve a comprehensive and effective evaluation of the network planning method.The simulation results show that the multi-level and multi-index oriented optical network planning and evaluation method proposed can improve the utilization of optical cable resources and the site ring rate,reduce the network construction cost,ensure the network reliability,and help to improve the efficiency and quality of network operation and maintenance.

So far,existing works related to quality evaluation of channel mainly focused on equipment layer and network layer in optical access network.Most of researches lack synthesis evaluation methods covering physical layer,network layer and business layer.As a result,it is hard for administrators to judge the actual quality of channel by network monitoring completely and accurately.To solve this problem,key influence factors of channel quality in optical access network was analyzed firstly,and a comprehensive multi-layer-multi-index evaluation model of channel quality was proposed.Then a SA-BP neural network algorithm was designed to train the parameters of the proposed evaluation model to make the result more accurate.The simulation results show that the proposed method has higher accuracy and stability,and can improve the quality and efficiency of network operation and maintenance.

Network virtualization is becoming one of the most promising ways to solve resource solidification problem of fiber-wireless access networks. To fully utilize substrate resources, a virtual resource embedding method including three sub-mechanisms is presented in this paper. The first one is the priority-based virtual network request (VNR) selecting mechanism, which sorts VNRs and preferentially selects higher priority VNRs for embedding. Then a dynamic embedding mechanism based on load balancing is presented to increase the acceptance rate of VNRs. Second, when embedding is saturated, an embedding update mechanism is proposed to deal with the arrival of new VNRs. It promotes the acceptance rate of high-priority VNRs and the revenue of the infrastructure provider (InP). Finally, a backup resource sharing mechanism is designed to improve reliability, which enables backup resources to be shared and responds to the failures with minimal resources. The simulation results show that the proposed embedding method can balance the resource consumption better than the Greedy Node Embedding and Shortest-path Link Embedding Algorithm, as well as the Greedy Node Embedding and Maximum Residual Bandwidth Link Embedding Algorithm. Besides, by applying our proposed algorithm, higher VNR acceptance rate, InP revenue, and resource utilization are achieved.