Outcomes of treatment of severe COVID-19 pneumonia with tocilizumab: a report of two cases from Tunisia

The SARS CoV-2 pandemic is a global health threat with high morbidity and mortality (1 to 4%) rates. COVID-19 is correlated with important immune disorders, including a “cytokine storm”. A new therapeutic approach using the immunomodulatory drug, Anti-IL6 (tocilizimub), has been proposed to regulate it. We report here the first Tunisian experience using tocilizimub in two severe cases of COVID-19 pneumonia. The diagnosis was confirmed by chest scan tomography. Biological parameters showed a high level of Interleukin-6 (IL-6) that increased significantly during hospitalization. The patients developed hypoxia, so they received intravenously 8 mg/kg body weight tocilizumab. There was a resultant decrease in the level of IL6, with clinically good evolution. Blocking the cytokine IL-6 axis is a promising therapy for patients developing COVID-19 pathology

Enquête sur l'applicabilité de l'apprentissage en profondeur et de la chaîne de blocs pour l'Internet des objets défini par logiciel

5G mobile network has seen phenomenal growth in providing IoT services and applications. IoT devices are often battery-powered to perform their operations autonomously and serve a variety of situations, such as smart cities, autonomous cars, smart manufacturing, etc., thereby needing efficient energy consumption to extend their lifespan. IoT networks should provide i) an on-demand resource allocation to support adaptive horizontal and vertical scaling of the network resources; ii) flexible infrastructure virtualization that exploits in-network programmability capabilities to operate inside an SDN-enabled virtualization platform; iii) a device-driven and human-driven intelligence to address the issues of energy efficiency and ultra-low latency requirements for future reliable and real-time IoT applications. Despite the promise, IoT networks face several challenging issues stemming from resource constraints and low-computation performance. Additionally, IoT systems encounter several security and privacy concerns to prevent unauthorized access to smart devices and secure trust-less interactions between devices themselves and service providers on the Internet.To address this plethora of challenges, this thesis presents an energy-efficiency IoT system, less computation-intensive, easy to implement, and amenable to online adaptation to the variations of the network condition. In the first contribution, we introduce a novel IoT network virtualization approach based on SDN/NFV to offer a high degree of automation in service chaining delivery for IoT devices. The second contribution introduces a Deep Reinforcement Learning energy-efficient task assignment and scheduling in SDN-based fog IoT Network. Furthermore, we present a computing model for reducing network latency and traffic overhead by centralizing the network control and orchestration in a single SDN controller layer. The last contribution introduces a deep learning approach that combines SDN and blockchain to achieve task scheduling and offloading, improve the response rate of IoT services to offer high levelsof performance, and strive to perform dynamic resource management.Le réseau mobile 5G a connu une croissance phénoménale dans la fourniture de services et d'applications IoT. Les appareils IoT sont souvent alimentés par batterie pour effectuer leurs opérations de manière autonome et servir à diverses situations, telles que les villes intelligentes, les voitures autonomes, la fabrication intelligente, etc. ont donc besoin d'une consommation d'énergie efficace pour prolonger leur durée de vie. Les réseaux IoT devraient fournir: i) une allocation de ressources à la demande pour prendre en charge une mise à l'échelle horizontale et verticale adaptative des ressources du réseau; ii) une virtualisation d'infrastructure flexible qui exploite les capacités de programmabilité en réseau pour fonctionner à l'intérieur d'une plate-forme de virtualisation compatible SDN; iii) une intelligence pilotée par les appareils et pilotée par l'homme pour répondre aux problèmes d'efficacité énergétique et aux exigences de latence ultra-faible pour les futures applications IoT fiables et en temps réel. Malgré la promesse, le réseau IoT est confronté à plusieurs problèmes complexes liés à ses contraintes de ressources et à ses faibles performances de calcul. De plus, les systèmes IoT rencontrent plusieurs problèmes de sécurité et de confidentialité pour empêcher l'accès non autorisé aux appareils intelligents et pour sécuriser les interactions sans confiance entre les appareils eux-mêmes et avec les fournisseurs de services sur Internet.Pour relever cette pléthore de défis, cette thèse présente un système IoT à haut rendement énergétique, moins gourmand en calculs, facile à mettre en œuvre et pouvant être adapté en ligne aux variations de l'état du réseau. Dans la première contribution, nous introduisons une nouvelle approche de virtualisation de réseau IoT basée sur SDN/NFV pour offrir un degré élevé d'automatisation dans la prestation de chaînage de services pour les appareils IoT. Dans la deuxième contribution, nous introduisons une attribution et une planification des tâches économes en énergie par Apprentissage par Renforcement dans un réseau IoT de brouillard basé sur SDN. Nous présentons un modèle informatique pour réduire la latence du réseau et la surcharge de trafic en centralisant le contrôle et l'orchestration du réseau dans une seule couche de contrôleur SDN. La dernière contribution introduit une approche d'apprentissage en profondeur qui combine SDN et blockchain pour réaliser la planification et le déchargement des tâches, améliorer le taux de réponse des services IoT pour offrir des niveaux de performance élevés et s'efforcer d'effectuer une gestion dynamique des ressources

Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network

5G mobile network services have made tremendous growth in the IoT network. As a result, a counters number of battery-powered IoT devices are deployed to serve diverse scenarios, e.g., smart cities, autonomous farming, smart manufacturing, to name but a few. In this context, energy consumption became one of the most critical concerns in interconnecting smart IoT devices in such scenarios. Additionally, whenever these IoT devices are distributed in space and time-evolving, they are expected to deliver high volume data scalably/predictably while minimizing end-to-end latency. Furthermore, edge IoT nodes often face the biggest hurdle of performing optimal resource distribution and achieving high-performance levels while coping with task handling, energy conservation, and ultra-reliable low-latency variability. This paper investigates an energy-aware and low-latency oriented computing task scheduling problem in a Software-Defined Fog-IoT Network. We formulate the online task assignment and scheduling problem as an energy-constrained Deep Q-Learning process as a kickoff. The latter strives to minimize the network latency while ensuring energy efficiency by saving battery power under the constraints of application dependence. Then, given the task arrival process, we introduce a deep reinforcement learning (DRL) approach for dynamic task scheduling and assignment in SDN-enabled edge networks. We conducted comprehensive experiments and compared the presented algorithm to three pioneering deep learning algorithms (i.e., deterministic, random, and A3C agents). Extensive simulation results demonstrated that our proposed solution outperforms these algorithms. Additionally, we highlight the characterizing feature of our design, energy-awareness, as it offers better energy-saving by up to 87% compared against the other approaches. We have shown that the offloading scheme could perform more tasks with the available battery power by up to 50% more minor time delay. Our results support our claims that the solution we propose can readily be used to dynamically optimize task scheduling and assignment of complex jobs with task dependencies in distributed Fog IoT networks

Managing Wireless Fog Networks using Software-Defined Networking

International audienceFog computing has recently emerged as a new cyber foraging technique to offload resource-intensive tasks from mobile devices to mobile cloudlets in close proximity to end-users. Since the one-hop communication in the network edge is predominantly wireless, Wireless Mesh Networks (WMNs) are being considered to build wireless fog networks. However, WMNs use distributed hop-by-hop routing protocols to reflect a partial visibility of the network, which limits their ability to perform global network management and monitoring needed by fog networks. Software Defined Networking (SDN) provides a centralized control and management of the entire network, which makes it a good candidate to support fog communication. Unfortunately, the SDN OpenFlow protocol does not support any functionalities for wireless fog networks as it is primarily targeted to wired networks. To address these issues, this paper presents a SDN-enabled wireless fog architecture that combines both OpenFlow and distributed wireless protocols. The proposed solution provides lower latency and efficient load balancing to offload the network load by enabling programmable fog routers

Accurate prediction of international trade flows: Leveraging knowledge graphs and their embeddings

Knowledge representation (KR) is vital in designing symbolic notations to represent real-world facts and facilitate automated decision-making tasks. Knowledge graphs (KGs) have emerged so far as a popular form of KR, offering a contextual and human-like representation of knowledge. In international economics, KGs have proven valuable in capturing complex interactions between commodities, companies, and countries. By putting the gravity model, which is a common economic framework, into the process of building KGs, important factors that affect trade relationships can be taken into account, making it possible to predict international trade patterns. This paper proposes an approach that leverages Knowledge Graph embeddings for modeling international trade, focusing on link prediction using embeddings. Thus, valuable insights are offered to policymakers, businesses, and economists, enabling them to anticipate the effects of changes in the international trade system. Moreover, the integration of traditional machine learning methods with KG embeddings, such as decision trees and graph neural networks are also explored. The research findings demonstrate the potential for improving prediction accuracy and provide insights into embedding explainability in knowledge representation. The paper also presents a comprehensive analysis of the influence of embedding methods on other intelligent algorithms