Service function chain (SFC) has been recognized as one of the most important technologies that can satisfy dynamic service demands in the edge clouds. However, how to efficiently embed SFCs in the dynamic edge-cloud scenarios remains as a challenging problem. Considering different network topologies, we devise two deep reinforcement learning (DRL)-based methods for two network sizes: a deep deterministic policy gradient (DDPG) based method for the small-scale networks and an asynchronous advantage actor-critic (A3C) based approach for the large-scale networks. Simulation results demonstrate that our proposals can efficiently deal with the SFC-DMP in edge clouds and outperform the state-of-the-art methods in terms of the delay.

Network Function Virtualization (NFV) technology continues gaining more attention as a paradigm shift, and telecommunication services can be flexibly deployed and managed. Any service can be represented by a Service Function Chain (SFC) that is a set of Virtual Network Functions (VNFs) to be executed based on the strict order. The NFV-enabled SFCs applied in the future Internet of Things (IoT) networks emerge a challenging problem, particularly more and more IoT devices are trying to access their telecommunication services whenever and wherever, SFCs are needed to be dynamically and adaptively reconfigured, thus adapting to the service requests’ dynamics for lower resource consumption and higher revenue for Internet Service Providers (ISPs). In this paper, we study the service function chain dynamic reconfiguration problem (SFC-DRP) in the IoT networks, a discrete time Markov decision process (DTMDP) based IoT SFC-DRP is formulated by guaranteeing the QoS and resource constraints. We subsequently propose a novel Deep Dyna-Q (DDQ) approach to solve this model. Our proposal has been evaluated with the obtained results demonstrating an average CPU root mean square error (RMSE) of 0.17, compared to 0.75 obtained while using the original approach. Moreover, our proposed SFC reconfiguration technique can approximate the performance of the integer linear programming (ILP) model within a polynomial time, and outperform the existing benchmarks in terms of the reconfiguration overhead and the resource utilization ratio from service provisioning respectively.

Aims and Objectives: To determine the frequency, timing, and duration of post‐acute sequelae of SARS‐CoV‐2 infection (PASC) and their impact on health and function.Background: Post‐acute sequelae of SARS‐CoV‐2 infection is an emerging major public health problem that is poorly understood and has no current treatment or cure. PASC is a new syndrome that has yet to be fully clinically characterised.Design: Descriptive cross‐sectional survey (n = 5163) was conducted from online COVID‐19 survivor support groups who reported symptoms for more than 21 days following SARS‐CoV‐2 infection.Methods: Participants reported background demographics and the date and method of their covid diagnosis, as well as all symptoms experienced since onset of covid in terms of the symptom start date, duration, and Likert scales measuring three symptom‐specific health impacts: pain and discomfort, work impairment, and social impairment. Descriptive statistics and measures of central tendencies were computed for participant demographics and symptom data.Results: Participants reported experiencing a mean of 21 symptoms (range 1–93); fatigue (79.0%), headache (55.3%), shortness of breath (55.3%) and difficulty concentrating (53.6%) were the most common. Symptoms often remitted and relapsed for extended periods of time (duration M = 112 days), longest lasting symptoms included the inability to exercise (M = 106.5 days), fatigue (M = 101.7 days) and difficulty concentrating, associated with memory impairment (M = 101.1 days). Participants reported extreme pressure at the base of the head, syncope, sharp or sudden chest pain, and “brain pressure” among the most distressing and impacting daily life.Conclusions: Post‐acute sequelae of SARS‐CoV‐2 infection can be characterised by a wide range of symptoms, many of which cause moderate‐to‐severe distress and can hinder survivors' overall well‐being.Relevance to Clinical Practice: This study advances our understanding of the symptoms of PASC and their health impacts.

Unmanned aerial vehicle (UAV) networks have a wide range of applications, such as in the Internet of Things (IoT), 5G communications, and so forth. However, the communications between UAVs and UAVs to ground control stations mainly use radio channels, and therefore these communications are vulnerable to cyberattacks. With the advent of software-defined radio (SDR), smart attacks that can flexibly select attack strategies according to the defender’s state information are gradually attracting the attention of researchers and potential attackers of UAV networks. The smart attack can even induce the defender to take a specific defense strategy, causing even greater damage. Inspired by symmetrical thinking, a solution using a software-defined network (SDN) to combat software-defined radio was proposed. We propose a network architecture which uses dual controllers, including a UAV flight controller and SDN controller, to achieve collaborative decision-making. Built on the top of the SDN, the state information of the whole network converges quickly and is fitted to an environment model used to develop an improved Dyna-Q-based reinforcement learning algorithm. The improved algorithm integrates the power allocation and track planning of UAVs into a unified action space. The simulation data showed that the proposed communication solution can effectively avoid smart jamming attacks and has faster learning efficiency and higher convergence performance than the compared algorithms.

Network function virtualization and multiaccess edge computing have been introduced by Internet service providers to deal with various challenges, which hinder them from satisfying the increasing demand of the low-latency network applications for the Internet of Things (IoT). In edge clouds, any network application for the IoT can be expressed as a service function chain (SFC) consisting of several strictly ordered virtual network functions (VNFs), which can be geographically placed onto edge clouds close to the terminals. However, regarding the large number of terminals and constantly dynamics of edge clouds, determining the placement of VNFs and routing service paths that optimizes the end-to-end delays is a challenging problem. This problem can be also called SFC dynamic orchestration problem. To exactly solve the problem, an integer linear programming (ILP) model is formulated. Then, it is difficult to apply the exact method to deal with the SFC dynamic orchestration problem in the large-scale networks, and this article presents a Lagrangian relaxation heuristic-based algorithm for the optimization, thus reducing the computational complexity. It is demonstrated that the proposed algorithm can efficiently achieve a near-optimal solution with a theoretical analysis. The obtained simulation results show that the proposed algorithm can approximate the performance of ILP’s solution and outperform the current benchmarks in terms of the end-to-end delay and service acceptance ratio.

Chronic obstructive pulmonary disease (COPD) is the most frequent chronic respiratory disease and a leading cause of morbidity and mortality, worldwide. Given that the foremost risk factor leading to the development of COPD is cigarette smoke, the initial treatment for COPD is smoking cessation. Even after smoking cessation, inflammation, apoptosis and oxidative stress can persist and continue to contribute to COPD. Although current therapies for COPD (which are primarily based on anti-inflammatory drugs such as corticosteroids, theophylline and bronchodilators) reduce airway obstruction, limit COPD exacerbation and improve the patient’s health-related quality-of-life, none can prevent disease progression or reduce mortality. Recent advances in stem cell research have provided novel insight into the potential of bone marrow mesenchymal stem cells (MSCs) in the treatment of several pulmonary diseases. This review article discusses the biological effects and mechanisms of action of MSC transplantation in COPD, and highlights the foundation that MSCs provide for novel therapeutic approaches in COPD.