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The use of solar electric vehicles (SEV) has recently been the subject of frantic communication by many researchers in this sector such as researcher (GASBAOUI Brahim and F. Burling). In this research, we focused on adopting other renewable energy sources, such as wind energy, to convert this energy into kinetic energy for the vehicle (EVWTG electric vehicle). The main aim of this research is to adopt the principle of a wind turbine generator in order to power an electric vehicle (EV), which relies on energy of the air currents on the surface of the vehicle and converts them into electrical energy to feed the electric motor simultaneously or storage by batteries. This system has several draw backs such as continuous energy production and controlling the speed of the wind turbine. For this, we developed a technique to ensure the production of electrical energy and its management according to the proposed system energy management technology. The operation model of the proposed EVWTG system was simulated using MATLAB, where the results obtained proved the effectiveness of the proposed control and energy management, which can be used in other practical studies in the future.

Photovoltaic (PVS) generators’ nonlinear electrical characteristics allow for greater performance and efficiency when they are forced to operate at their peak power (MPP). This article suggests an adaptive method for maximizing power point tracking that makes use of artificial neural network (ANN) techniques (MPPT). A step-up converter powered by a separate solar generator is under the control of an ANN controller built on a neural network training database (PVS). The results show that ANN-MPPT has good control performance and is near to the maximum power point of PVS when compared to conventional MPPT methods like perturb and observe and incremental conductance.

The cork oak (Quercus suber L.) forests are ecosystems of high environmental and socioeconomic value in the Mediterranean basin. However, in Algeria, the cork oak forests are highly threatened by repeated fires, overgrazing and the anthropogenic pressure that weaken the ecosystem of cork oak and affect its natural regeneration. This degradation results in an alteration of many ecological components of the cork oak, such as fungal communities in the soil. Thus, the aim of this work is to study the effects of cork oak stand degradation on the diversity of mycorrhizal communities associated with Quercus suber and some of its understory shrubs (Cistus monspeliensis, Lavandula stoechas and Thymus vulgaris) in the forest of Brabtia (northeastern Algeria).For this purpose, two sites were chosen: one degraded by the anthropogenic factors and the other non-degraded one. Moreover, it is suggested that some plants of the understory shrub vegetation of cork oak, such as the cistus, lavender and thyme, prove to be favourable to the juvenile growth of this tree. The results obtained showed that the ectomycorrhizal (EcM) root colonization of cistus was higher compared to that of cork oak in both stations. The estimation of arbuscular mycorrhizal (AM) colonization showed significantly higher levels in the roots of cork oak, cistus, lavender and thyme in the degraded station compared with the non-degraded stations. In contrast, the rhizosphere soils of cork oak and cistus had low number of AM propagules and fungal spores, while under the roots of lavender and thyme, these two parameters were greatly improved with the abundance of the genera Glomus and Gigaspora. These results underline the need to take into account the plant–fungus interactions in the development of restoration strategies of the degraded soils and forest ecosystems.

In this article, a miniaturized patch antenna using fractal DGS and SRR DMS for GSM1800, C band and WiMAX applications is presented and investigated. Antenna miniaturization and multiband are accomplished by employing a fractal loop on the DGS structure and a split ring resonator slot in the patch. The proposed radiator has a hexagonal shape that operates at 6.8 GHz. To achieve downsize of 20.58, 38.22, 55.14 and 72.75% an antenna is designed using DGS and DMS structures for quad-band application appearing at 1.85, 3.05, 4.2, and 5.4 GHz. Simulated and tested data are investigated. The prototype antenna has adequate advantages, with a simple structure, compact size, multiband, and uniform radiation patterns.

Water plays a crucial role in the life and development of humanity. Since time immemorial, people have settled near water points, which have always been a source of life and well-being. For thousands of years, it has been celebrated and recognized as a sacred resource. Today, due to rapid population growth, economic development and other challenges that affect natural resources, water has become a precious commodity. To this end, the objective of our work is to know the chemism of the groundwater of the lower valley of Oued Bounamoussa Willaya of El Tarf (North East Algeria) and to diagnose the origin and intensity of the pollution that degrades this water resource. A sampling of water was carried out in February 2021 on all the length of Oued Bounamoussa (Upstream, Center and Downstream), of which several physico-chemical parameters were carried out such as; pH, EC, SS, NO-3, NO-2, Cl-, P2O5. The results obtained show the presence of a very important pollution rate in the groundwater of Oued Bounamoussa, which pollution has several origins namely urban, agricultural and industrial. So this water resource has become a danger and risk on the health of living beings and on the intoxication and it disturbs the sustainable development.

International audience; An efficient and scalable monitoring system is a critical component for any network to monitor and validate the functioning of the running services and the underlying infrastructure. This is more valid in 5G, as it relies on the network slicing concept, which adds many challenges to the monitoring system. Besides data isolation and multi-tenancy support, network slices require monitoring different types of resources, like RAN, computing, memory, network data rate, which belong to different technological domains, managed by different entities. Moreover, the monitoring system needs to be scalable, as in 5G a high-number of running network slices is envisioned. In this paper, we devise a novel monitoring framework for network slicing ready mobile networks, which features: 1) scalable monitoring system that supports a high number of running network slices in parallel; 2) technological domain agnostic thanks to a novel data collection (or monitoring) communication protocol; 3) support of multi-tenancy in a cloudnative environment. The framework has been implemented in a 5G facility, and its performance has been extensively evaluated.

Network Management as Code is presented to manage the infrastructure and deployment of control functions in a declarative manner by adopting GitOps in open disaggregated optical networks. We introduce an Automation Engine that analyzes topology changes to trigger the Continuous Delivery procedure of SDN applications on a micro-service architecture.

The emerging 5G networks promise to support novel network services with different requirements in terms of Quality of Service (QoS), such as low-latency and high bandwidth. Thanks to the network slicing concept, 5G is able to fulfill these different requirements while sharing the same physical infrastructure. Although network slicing is gaining maturity, slicing the Radio Access Network (RAN) is still challenging, particularly with the emergence of new physical features added by 5G New Radio (NR), such as Bandwidth part (BWP) and physical numerology. In this paper, we introduce a new framework, namely New Radio flexibility (NRflex), which addresses the challenge of slicing the RAN in 5G. NRflex provides a solution that dynamically assigns BWP to the running slices and their associated User Equipment (UE), aiming to fulfill the slices’ required QoS. Simulation results showed the superiority of NRflex to meet network slice requirements while optimizing the 5G RAN resources, compared to other existing solutions.

The recent O-RAN specifications promote the evolution of RAN architecture by function disaggregation, adoption of open interfaces, and instantiation of a hierarchical closed-loop control architecture managed by RAN Intelligent Controllers (RICs) entities. This paves the road to novel data-driven network management approaches based on programmable logic. Aided by Artificial Intelligence (AI) and Machine Learning (ML), novel solutions targeting traditionally unsolved RAN management issues can be devised. Nevertheless, the adoption of such smart and autonomous systems is limited by the current inability of human operators to understand the decision process of such AI/ML solutions, affecting their trust in such novel tools. eXplainable AI (XAI) aims at solving this issue, enabling human users to better understand and effectively manage the emerging generation of artificially intelligent schemes, reducing the human-to-machine barrier. In this survey, we provide a summary of the XAI methods and metrics before studying their deployment over the O-RAN Alliance RAN architecture along with its main building blocks. We then present various use-cases and discuss the automation of XAI pipelines for O-RAN as well as the underlying security aspects. We also review some projects/standards that tackle this area. Finally, we identify different challenges and research directions that may arise from the heavy adoption of AI/ML decision entities in this context, focusing on how XAI can help to interpret, understand, and improve trust in O-RAN operational networks., Comment: 33 pages, 13 figures

Tuberculous Spondylodiscitis is the most common form of musculoskeletal tuberculosis. Molecular techniques on fresh tissues are proved to improve the diagnosis of tuberculous spondylodiscitis and to allow a rapid diagnosis to initiate the treatment and prevent neurological complications.The objective of the present study was to assess the diagnostic performances of single tube nested PCR and GeneXpert ultra in the diagnosis of tuberculous spondylodiscitis on formalin fixed paraffin embedded tissues.This study included 63 tuberculous spondylodiscitis cases collected from June 2014 to January 2020 and corresponding to 27 definite tuberculous spondylodiscitis with positive microbiology, and 36 probable tuberculous spondylodiscitis, with histopathological, clinical and radiological findings consistent with tuberculous spondylodiscitis but with negative microbiology. The sensitivity, specificity, positive predictive value and negative predictive value of nested PCR and GeneXpert ultra were determined with reference to microbiology.Nested PCR was positive in 47 (75%) cases: 26/27 definite tuberculous spondylodiscitis and 21/36 probable tuberculous spondylodiscitis. GeneXpert ultra was positive in only 6 (10%) cases corresponding to definite tuberculous spondylodiscitis. The sensitivity, specificity, positive predictive value and negative predictive value of nested PCR on formalin fixed paraffin embedded tissues were 96%, 100%, 100%, 83% respectively. For GeneXpert ultra, these rates were 22%, 100%, 100% and 25% respectively.Nested PCR and GeneXpert ultra on formalin fixed paraffin embedded tissues are useful tools for the diagnosis of tuberculous spondylodiscitis, especially for cases where microbiological investigations were not carried out. Both techniques have excellent specificity but single tube nested PCR is more sensitive. Key Points • Molecular techniques are routinely performed on fresh tissues • GeneXpert and nested PCR on formalin fixed paraffin embedded tissues are reliable for the diagnosis of tuberculous spondylodiscitis • Nested PCR is more sensitive than Genexpert for diagnosing tuberculous spondylodiscitis.

Collision detection and avoidance between vehicles is one of the key services envisioned in the Internet of Vehicles. Such services are usually deployed at the multi-access edge computing (MEC) to ensure low-latency communication and thus guarantee real-time reactions to avoid collisions between vehicles. In order to maximize the coverage of the road and ensure that all vehicles are connected to an optimal MEC host (in terms of geographical location), the collision avoidance application needs to be instantiated on all the MEC hosts. This may add a burden on the computing resources available at the latter. In this article, we propose an AI-empowered framework that aims to optimize the computing resources at the MEC hosts. Our framework uses deep learning to predict the vehicle density to be served by a MEC host and derive the exact computing resources required by the collision detection application to run optimally. We evaluate the proposed framework using a real dataset representing vehicle mobility in a big city. Obtained results show the accuracy of our prediction model, and hence the efficiency of our resources assignment framework to exactly deduce the optimal computing resources needed by each instance of the application.

The increased flexibility and interconnectivity of modern industrial communication networks, obtained through the use of innovative technologies like network function virtualization and software-defined networking, require a secure and manageable framework to support the new communication and computing needs. To focus on these requirements, this article proposes a framework for reliable placement of services across physically separated locations, which offers both system optimization, in terms of latency and resource utilization, and connectivity policy enforcement to guarantee service reliability, safety, and security. This is achieved by exploiting a new approach to solve the virtual network embedding problem, using optimization modulo theories (MaxSMT), which allows the use of very expressive constraints.

Intramyocardial cartilage has never been reported in the human heart before. In the literature, the only reported localizations of cartilage in the heart were in the central fibrous body and the valves. We report a case of an unusual presence of cartilage tissue within the myocardial wall of the left ventricle in a 10-year-old boy who died unexpectedly. This case presents an interesting, unusual and apparently asymptomatic sudden cardiac death related to a cartilaginous myocardial tumor. Conducting system disturbance secondary to the myocardial tumor is the probable cause of death. This case is relevant not only for its singularity and originality, but also for the diverse and controversial hypotheses related to the onset of cartilaginous tissue in the myocardial wall. Early detection of this tumor by modern thoracic imaging may have prevented a fatal unexpected outcome.

In this work, a nonlinear dynamic model of an FZG-A10 spur gear was investigated by taking into account for the actual time-varying gear mesh stiffness and the frictional effects between meshing gear teeth to evaluate the influence of the dynamic effects on frictional gear power loss predictions. The equations of motion of the generalized translational-torsional coupled dynamic system derived from Lagrange principle was extended compared to authors’ previous work in order to account for time dependent coefficient of friction and profile errors. The dynamic response of spur gears, computed by an iterative implicit scheme of Newmark, is changed due to the presence of coefficient of friction and profile errors. A dynamic analysis was performed and the influence of frictional effect including tooth shape deviations, in particular, was scrutinized since a time-dependent coefficient of friction is deeply related to the gear surface roughness and all parameters dependent on gears error profiles are introduced in the proposed model. The predicted meshing gear power losses with constant and local friction coefficient were compared. The influence of constant and variable profile errors considered in the local coefficient of friction formulation was also studied and their corresponding root mean square (RMS) power loss was compared to the experimental results. The results using FZG A10 spur gear pairs running under several operating conditions (different loads and speeds) validate the superiority of the proposed model against previous similar models.

COVID-19 virus has strongly impacted our everyday life. Without the availability of a vaccine or a well-established and efficient treatment, we have to live with it. One way to mitigate the propagation of the virus is to respect social distancing between persons. Indeed, many governments have adopted it as one of the key solutions to reduce the propagation of the virus. However, it is difficult to enforce social distancing among the population. In this article, we propose to combine IoT and multi-access edge computing (MEC) technologies to build a service that checks and warns people in near real time if they are not practicing social distancing. The proposed service is composed of a client application side installed on the users' smartphone, which periodically sends GPS coordinates to remote servers sitting at the edge of the network (i.e., at MEC). The remote servers use a local algorithm to detect and warn users who are not practicing social distancing. The proposed service respects privacy and anonymity by hiding the user identity, and is capable of warning users in near real time thanks to the usage of MEC.

Brucellar spondylodiscitis (BS) is the most common form of musculoskeletal brucellosis. The isolation of Brucella spp from blood, other body fluids or tissue cultures is the gold standard for definitive diagnosis of BS. BS shows a large histopathological spectrum of lesions with non-specific and granulomatous forms, and its histopathological features are not widely reported. This case series study reported the histopathological features of a series of 21 BS. Ten cases revealed nonspecific forms of BS. Lymphocytes were the predominant inflammatory cells in this group. In one case neutrophils were predominant. The eleven remaining were granulomatous. The 11 exhibited histiocytic type granulomas. Ten were non-necrotizing. One case, taken from abscess wall, contained minimal deposition of caseous like necrotic material. This case and 2 others corresponding to abscess wall biopsies, showed histiocytic granulomas mixed with small aggregates of epithelioid cells without well-formed epithelioid granuloma nor giant cell. The histopathological diagnosis of BS is challenging. Nonspecific forms of BS, mimiking pyogenic spondylodiscitis, are observed in about half of the cases. Although nonspecific forms of BS are characterized by the predominance of lymphocytes and plasmocytes, BS forms with predominant neutrophil cell infiltration do exist. Histiocytic granuloma is highly suggestive of BS. Purely epithelioid forms of BS were not observed in the present series. Caseous like necrosis suggestive of tuberculosis is a possible feature of BS.

The Internet of Things (IoT) undergoes a rapid transformation this last decade, thanks to the appearance of low-power wide area network technologies, such as LoRa/LoRaWAN, SigFox, and narrowband IoT, which allow reducing the deployment cost of sensors and other IoT devices. Many emerging services such as smart city, Industry 4.0, and autonomous driving are based on IoT devices and applications to collect and analyze data and control end devices (i.e., actuators). Among these services, several IoT applications, such as data analytics, need to be deployed at the edge to either reduce the latency to access data or treat the high amount of generated data locally. However, in the context of LoRa/LoRaWAN, most of the current IoT service deployments run the applications at a central cloud to ease the integration with existing software as a service (SaaS) platforms, without exploiting the benefits of edge computing. In this article, we propose a new framework that leverages the ETSI multi-access edge computing (MEC) model to deploy LoRabased IoT applications at the edge. In particular, the proposed model takes advantage of the ETSI MEC features, such as dynamic deployment of an IoT application at the edge and application life cycle management. In addition, the proposed framework allows running an IoT application as a 5G network slice at the edge.

Learning from massive network data to produce cognitive knowledge for efficient resource management in 5G and beyond 5G (B5G) is still challenging. We are delighted to introduce the readers to this special section of the IEEE Transactions on Cognitive Communications and Networking (TCCN), which aims at exploring recent advances and addressing practical challenges in the intelligent resource management in 5G/B5G. We have received a total number of 30 submissions, and after a rigorous review process, 15 articles have been selected for publication, which are briefly discussed as follows.

AIMS: In this study, we aimed to explore the enzymatic diversity, the entomopathogenic and the antimicrobial potentialities of fungi associated with the pistachio bark beetle, Chaetoptelius vestitus. METHODS AND RESULTS: A total of 40 isolates were screened for enzymatic diversity. Most of them, 92·5%, were able to produce at least two of the screened enzymes. Pathogenic assays performed on C. vestitus showed a high entomopathogenic activity of the isolates Ata_io_1 (A. tamarii), Fve_io_1 (F. verticillioides), Tpi_io_1 (T. pinophilus), Pal_io_1 (P. album), Pbi_io_2 (Penicillium bilaiae) and Pch_io_1 (P. chrysogenum), as based on mean mortality of C. vestitus. A screening of antimicrobial activity using well diffusion method showed that the isolates Tro_io_1 (T. pinophilus), Tat_io_1 (T. atroroseus) and Pch_io_1 (P. chrysogenum) presented the highest antibacterial activity. Furthermore, Mgu_io_1 (M. guilliermondii), Asc_io_1 (A. sclerotiorum), Ata_io_1 (A. tamarii), G. lavendula (Gla_io_1), Pva_io_1 (P. variotii), Pul_io_1 (P. ulaiense), Tat_io_1 (T. atroroseus) and Tro_io_1 (T. roseum) were active against at least two of the three tested fungal phytopathogens. CONCLUSIONS: Fungal isolates representing entomopathogenic activity and a broad spectrum of antimicrobial activities can be considered as promising resources for biological pistachio trees protection. SIGNIFICANCE AND IMPACT OF THE STUDY: Fungi associated with C. vestitus were investigated for detecting their potential biotechnological applications.

6G systems are expected to serve a massive number of extremely heterogeneous network slices that cross multiple technological domains (i.e., RAN, edge, cloud, and core), posing significant challenges to classical centralized management and orchestration approaches in terms of scalability and sustainability. Within this context, a distributed and intelligent management and orchestration system is mandatory. This article proposes a novel framework featuring a distributed and AI-driven management and orchestration system for massive deployment of network slices in 6G. The proposed framework is compliant with both ETSI standards focusing on autonomous and intelligent network management and orchestration, that is, Zero touch Service Management (ZSM) and Experimental Networked Intelligent (ENI), leveraging their visions to enable autonomous as well as scalable management and orchestration of network slices and their dedicated resources. © 2002-2012 IEEE.

6G systems are expected to serve a massive number of extremely heterogeneous network slices that cross multiple technological domains (i.e., RAN, edge, cloud, and core), posing significant challenges to classical centralized management and orchestration approaches in terms of scalability and sustainability. Within this context, a distributed and intelligent management and orchestration system is mandatory. This article proposes a novel framework featuring a distributed and AI-driven management and orchestration system for massive deployment of network slices in 6G. The proposed framework is compliant with both ETSI standards focusing on autonomous and intelligent network management and orchestration, that is, Zero touch Service Management (ZSM) and Experimental Networked Intelligent (ENI), leveraging their visions to enable autonomous as well as scalable management and orchestration of network slices and their dedicated resources. © 2002-2012 IEEE.

Service Function Chaining (SFC) is the process of decomposing a network service into multiple functions that successively process packets to deliver the end-to-end service. In a multi-domain context, SFC placement is a challenging problem due to limited knowledge of the infrastructure of the local domains, which complicates the process of finding the optimal placement solutions. On the other hand, Reinforcement Learning has gained momentum as a tool for decision-making, allowing agents to construct and improve policies using feedback from the environment. In this paper, we leverage Deep Reinforcement Learning (DRL) to perform SFC placement on multiple domains. We devise a hierarchical architecture where the local domain agents and the multi-domain agent are trained using different DRL models to perform SFC and sub-SFC placement while satisfying the SLA requirements.

In this study, a frictional dynamic gear model is propounded for a lubricated FZG C40 spur gear pairs. The proposed nonlinear dynamic model accounts the actual time variable gear mesh stiffness with considering the frictional effects of meshing gear teeth. This developed model aims to predict with high accuracy the gear power losses and to highlight the influence of the friction coefficient. Two listed cases were considered through introducing a constant friction coefficient and a local coefficient of friction in the developed dynamic model. The influence of the constant and local coefficient of friction on the frictional spur gears power losses has been evaluated. All key parameters like axle gear oil dynamic and kinematic viscosity, contact conditions between meshing gear teeth through equivalent radius of curvature, slide-to-roll ratio, maximum Hertzian pressure and tooth profile deviations on which the local friction coefficient depends are introduced in the model formulation. The simulation results were obtained through solving the motion equations of the generalized translational torsional coupled dynamic system by an iterative implicit scheme of Newmark. The results of simulation were compared to experimental results investigated on an FZG test rig under several operating conditions of load and speed.

International audience; Information-Centric Networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today's Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, Named Data Networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is Intelligent Transportation System (ITS) realized through Vehicular Ad hoc NETwork (VANET) where vehicles exchange information and content with each other and with the infrastructure. To date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community.