Possible treatments of COVID-19 present and future

Since the outbreak of COVID-19 infection in December 2019, millions of people are infected, and thousands of people have died. Genetic shift and high infectivity rate made SARS-Cov-2 a pandemic. Doctors, researchers, and world leaders are scratching their heads, how to contained or treat the virus. Several treatment options are tried, but so far, there is no effective treatment available. Thousands of articles are published about COVID-19, and so much information is available that it is challenging for a practicing physician to review these articles in the limited time they have. This article summarized the treatment options for COVID-19 that have tried or are in clinical trials. The article also reviews other possibilities that are either briefly or not discussed in the literature but could play a role in the fight against COVID-19

Possible use of alcohol vapors by inhalation in the treatment of COVID-19 in clinical ill patients

Ethyl alcohol or ethanol is an effective chemical in denaturing enveloped viruses in vitro, including SARS-CoV-2. It is found that exposure of only 30 seconds is sufficient to deactivate the virus. Alcohol has never been used for the treatment of viral lung infection. However, alcohol vapors by inhalation are used for acute respiratory distress syndrome and alcohol withdrawals

Energy management in content distribution network servers

Les infrastructures Internet et l'installation d'appareils très gourmands en énergie (en raison de l'explosion du nombre d'internautes et de la concurrence entre les services efficaces offerts par Internet) se développent de manière exponentielle. Cela entraîne une augmentation importante de la consommation d'énergie. La gestion de l'énergie dans les systèmes de distribution de contenus à grande échelle joue un rôle déterminant dans la diminution de l'empreinte énergétique globale de l'industrie des TIC (Technologies de l'information et de la communication). Elle permet également de diminuer les coûts énergétiques d'un produit ou d'un service. Les CDN (Content Delivery Networks) sont parmi les systèmes de distribution à grande échelle les plus populaires, dans lesquels les requêtes des clients sont transférées vers des serveurs et traitées par des serveurs proxy ou le serveur d'origine, selon la disponibilité des contenus et la politique de redirection des CDN. Par conséquent, notre objectif principal est de proposer et de développer des mécanismes basés sur la simulation afin de concevoir des politiques de redirection des CDN. Ces politiques prendront la décision dynamique de réduire la consommation d'énergie des CDN. Enfin, nous analyserons son impact sur l'expérience utilisateur. Nous commencerons par une modélisation de l'utilisation des serveurs proxy et un modèle de consommation d'énergie des serveurs proxy basé sur leur utilisation. Nous ciblerons les politiques de redirection des CDN en proposant et en développant des politiques d'équilibre et de déséquilibre des charges (en utilisant la loi de Zipf) pour rediriger les requêtes des clients vers les serveurs. Nous avons pris en compte deux techniques de réduction de la consommation d'énergie : le DVFS (Dynamic Voltage Frequency Scaling) et la consolidation de serveurs. Nous avons appliqué ces techniques de réduction de la consommation d'énergie au contexte d'un CDN (au niveau d'un serveur proxy), mais aussi aux politiques d'équilibre et de déséquilibre des charges afin d'économiser l'énergie. Afin d'évaluer les politiques et les mécanismes que nous proposons, nous avons mis l'accent sur la manière de rendre l'utilisation des ressources des CDN plus efficace, mais nous nous sommes également intéressés à leur coût en énergie, à leur impact sur l'expérience utilisateur et sur la qualité de la gestion des infrastructures. Dans ce but, nous avons défini comme métriques d'évaluation l'utilisation des serveurs proxy, d'échec des requêtes comme les paramètres les plus importants. Nous avons transformé un simulateur d'événements discrets CDNsim en Green CDNsim, et évalué notre travail selon différents scénarios de CDN en modifiant : les infrastructures proxy des CDN (nombre de serveurs proxy), le trafic (nombre de requêtes clients) et l'intensité du trafic (fréquence des requêtes client) en prenant d'abord en compte les métriques d'évaluation mentionnées précédemment. Nous sommes les premiers à proposer un DVFS et la combinaison d'un DVFS avec la consolidation d'un environnement de simulation de CDN en prenant en compte les politiques d'équilibre et de déséquilibre des charges. Nous avons conclu que les techniques d'économie d'énergie permettent de réduire considérablement la consommation d'énergie mais dégradent l'expérience utilisateur. Nous avons montré que la technique de consolidation des serveurs est plus efficace dans la réduction d'énergie lorsque les serveurs proxy ne sont pas beaucoup chargés. Dans le même temps, il apparaît que l'impact du DVFS sur l'économie d'énergie est plus important lorsque les serveurs proxy sont bien chargés. La combinaison des deux (DVFS et consolidation des serveurs) permet de consommer moins d'énergie mais dégrade davantage l'expérience utilisateur que lorsque ces deux techniques sont utilisées séparément.Explosive increase in Internet infrastructure and installation of energy hungry devices because of huge increase in Internet users and competition of efficient Internet services causing a great increase in energy consumption. Energy management in large scale distributed systems has an important role to minimize the contribution of Information and Communication Technology (ICT) industry in global CO2 (Carbon Dioxide) footprint and to decrease the energy cost of a product or service. Content distribution Networks (CDNs) are one of the popular large scale distributed systems, in which client requests are forwarded towards servers and are fulfilled either by surrogate servers or by origin server, depending on contents availability and CDN redirection policy. Our main goal is therefore, to propose and to develop simulation-based principled mechanisms for the design of CDN redirection policies which will do and carry out dynamic decisions to reduce CDN energy consumption and then to analyze its impact on user experience constraints to provide services. We started from modeling surrogate server utilization and derived surrogate server energy consumption model based on its utilization. We targeted CDN redirection policies by proposing and developing load-balance and load-unbalance policies using Zipfian distribution, to redirect client requests to servers. We took into account two energy reduction techniques, Dynamic Voltage Frequency Scaling (DVFS) and server consolidation. We applied these energy reduction techniques in the context of a CDN at surrogate server level and injected them in load-balance and load-unbalance policies to have energy savings. In order to evaluate our proposed policies and mechanisms, we have emphasized, how efficiently the CDN resources are utilized, at what energy cost, its impact on user experience and on quality of infrastructure management. For that purpose, we have considered surrogate server's utilization, energy consumption, energy per request, mean response time, hit ratio and failed requests as evaluation metrics. In order to analyze energy reduction and its impact on user experience, energy consumption, mean response time and failed requests are considered more important parameters. We have transformed a discrete event simulator CDNsim into Green CDNsim and evaluated our proposed work in different scenarios of a CDN by changing: CDN surrogate infrastructure (number of surrogate servers), traffic load (number of client requests) and traffic intensity (client requests frequency) by taking into account previously discussed evaluation metrics. We are the first who proposed DVFS and the combination of DVFS and consolidation in a CDN simulation environment, considering load-balance and loadunbalance policies. We have concluded that energy reduction techniques offer considerable energy savings while user experience is degraded. We have exhibited that server consolidation technique performs better in energy reduction while surrogate servers are lightly loaded. While, DVFS impact is more considerable for energy gains when surrogate servers are well loaded. Impact of DVFS on user experience is lesser than that of server consolidation. Combination of both (DVFS and server consolidation) presents more energy savings at higher cost of user experience degradation in comparison when both are used individually

Cache-enabled Unmanned Aerial Vehicles for Cooperative Cognitive Radio Networks

Cooperative cognitive radio network is a new method to alleviate the spectrum scarcity problem. Proactive content caching and UAV relaying techniques are deployed in a CRN, to enable the achievable rates for primary and secondary systems. Even though these two emerging technologies are grateful to solve the problem of spectrum scarcity, there are still open issues to influence the system performance and the utilization of spectrum. In this article, we provide an overview of the cooperation technique, including their theoretical schemes and the advanced performance in radio networks. Then, this article proposes a cache-enabled UAV cooperation scheme in CRN, which enhances the CRN's transmission capability and reduces the redundant traffic load of CRN. The experimental results show that the cache-enabled UAV scheme significantly improves the achievable rates for both systems in CCRN. In addition, we present future work related to content caching, deployment of UAVs and CCRN to support radio networks

Susceptibility of Continual Learning Against Adversarial Attacks

Recent continual learning approaches have primarily focused on mitigating catastrophic forgetting. Nevertheless, two critical areas have remained relatively unexplored: 1) evaluating the robustness of proposed methods and 2) ensuring the security of learned tasks. This paper investigates the susceptibility of continually learned tasks, including current and previously acquired tasks, to adversarial attacks. Specifically, we have observed that any class belonging to any task can be easily targeted and misclassified as the desired target class of any other task. Such susceptibility or vulnerability of learned tasks to adversarial attacks raises profound concerns regarding data integrity and privacy. To assess the robustness of continual learning approaches, we consider continual learning approaches in all three scenarios, i.e., task-incremental learning, domain-incremental learning, and class-incremental learning. In this regard, we explore the robustness of three regularization-based methods, three replay-based approaches, and one hybrid technique that combines replay and exemplar approaches. We empirically demonstrated that in any setting of continual learning, any class, whether belonging to the current or previously learned tasks, is susceptible to misclassification. Our observations identify potential limitations of continual learning approaches against adversarial attacks and highlight that current continual learning algorithms could not be suitable for deployment in real-world settings.Comment: 18 pages, 13 figure

Planning Fog networks for time-critical IoT requests

The massive growth of the Internet of Things (IoT) applications and the challenges of Cloud computing have increased the importance of Fog networks for timely processing the requests from delay-sensitive applications. A Fog network provides local aggregation, analysis, and processing of IoT requests that may or may not be time-critical. One of the major issues of Fog is its capacity planning considering the traffic load of time-critical requests. The response time can be huge if a time-critical request is processed on Cloud. The response time of a time-critical request can be big on the Fog layer if it is not prioritized. Hence, there is a need to handle the time-critical traffic on a priority basis at the Fog layer. In this paper, a priority queuing model with preemption has been proposed considering the mixed types of requests at the Fog layer. The proposed approach determines the required number of Fog nodes in order to satisfy the desired Quality of Service (QoS) requirements of IoT requests. The proposed mechanism is evaluated through simulations using the iFogSim simulator. The work can be used in the capacity planning of Fog networks

Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images

The ongoing COVID-19 pandemic has already taken millions of lives and damaged economies across the globe. Most COVID-19 deaths and economic losses are reported from densely crowded cities. It is comprehensible that the effective control and prevention of epidemic/pandemic infectious diseases is vital. According to WHO, testing and diagnosis is the best strategy to control pandemics. Scientists worldwide are attempting to develop various innovative and cost-efficient methods to speed up the testing process. This paper comprehensively evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Moreover, it provides a comparative analysis of these models in terms of accuracy. This study identifies the effective methodologies to control and prevent infectious respiratory diseases. Our trained models have demonstrated outstanding results in classifying the COVID-19 infected chest x-rays. In particular, our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95\%, 95\%, and 94\% test set for COVID-19 class classification, respectively. Thus, it can be beneficial for clinical practitioners and radiologists to speed up the testing, detection, and follow-up of COVID-19 cases

Price-based demand response for household load management with interval uncertainty

In a smart grid, efficient load management can help balance and reduce the burden on the national power grid and also minimize local operational electricity cost. Robust optimization is a technique that is increasingly used in home energy management systems, where it is applied in the scheduling of household loads through demand side control. In this work, interruptible loads and thermostatically controlled loads are analyzed to obtain optimal schedules in the presence of uncertainty. Firstly, the uncertain parameters are represented as different intervals, and then in order to control the degree of conservatism, these parameters are divided into various robustness levels. The conventional scheduling problem is transformed into a deterministic scheduling problem by translating the intervals and robustness levels into constraints. We then apply Harris’ hawk optimization together with integer linear programming to further optimize the load scheduling. Cost and trade-off schemes are considered to analyze the financial consequences of several robustness levels. Results show that the proposed method is adaptable to user requirements and robust to the uncertainties