11 research outputs found

    A Novel Placement Algorithm for the Controllers Of the Virtual Networks (COVN) in SD-WAN with Multiple VNs

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    The escalation of communication demands and the emergence of new telecommunication concepts such as 5G cellular system and smart cities requires the consolidation of a flexible and manageable backbone network. These requirements motivated the researcher to come up with a new placement algorithm for the Controller of Virtual Network (COVN). This is because SDN and network virtualisation techniques (NFV and NV), are integrated to produce multiple virtual networks running on a single SD-WAN infrastructure, which serves the new backbone. One of the significant challenges of SD-WAN is determining the number and the locations of its controllers to optimise the network latency and reliability. This problem is fairly investigated and solved by several controller placement algorithms where the focus is only on physical controllers. The advent of the sliced SD-WAN produces a new challenge, which necessitates the SDWAN controllers (physical controller/hosted server) to run multiple instances of controllers (virtual controllers). Every virtual network is managed by its virtual controllers. This calls for an algorithm to determine the number and the positions of physical and virtual controllers of the multiple virtual SD-WANs. According to the literature review and to the best of the author knowledge, this problem is neither examined nor yet solved. To address this issue, the researcher designed a novel COVN placement algorithm to compute the controller placement of the physical controllers, then calculate the controller placement of every virtual SD-WAN independently, taking into consideration the controller placement of other virtual SD-WANs. COVN placement does not partition the SD-WAN when placing the physical controllers, unlike all previous placement algorithms. Instead, it identifies the nodes of the optimal reliability and latency to all switches of the network. Then, it partitions every VN separately to create its independent controller placement. COVN placement optimises the reliability and the latency according to the desired weights. It also maintains the load balancing and the optimal resources utilisation. Moreover, it supports the recovering of the controller failure. This novel algorithm is intensively evaluated using the produced COVN simulator and the developed Mininet emulator. The results indicate that COVN placement achieves the required optimisations mentioned above. Also, the implementations disclose that COVN placement can compute the controller placement for a large network ( 754 switches) in very small computation time (49.53 s). In addition, COVN placement is compared to POCO algorithm. The outcome reveals that COVN placement provides better reliability in about 30.76% and a bit higher latency in about 1.38%. Further, it surpasses POCO by constructing the balanced clusters according to the switch loads and offering the more efficient placement to recover controller-failure

    Developing an asynchronous technique to evaluate the performance of SDN HP Aruba switch and OVS

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    Developers of Software Defined Network (SDN) faces a lack of or difficulty in getting a physical environment to test their inventions and developments. That drives them to use a virtual environment for their experiments. This work addresses the differences between the SDN virtual environment and physical SDN switches, which leads to equip a more realistic SDN virtual environment. Consequently, this paper presents a precise performance evaluation and comparison of off-the-shelf SDN devices, HP Aruba 3810M, with Open Virtual Switch (OVS) inside Mininet emulator. This work examines the variability of the path delay, throughput, packet losses and jitter of SDN in a different windows size of the packets and network background loads. Our conducted experiments consider a number of protocols such as ICMP, TCP and UDP. In order to evaluate the network latency accurately, a new asynchronous latency measurement technique is proposed. The developed technique shows more precise results in comparison to other techniques. Furthermore, the work focuses on extracting the flow-setup latency, caused by the external SDN controller when setting flow rules into the switch. The comparison of results shows a dissimilarity in the behaviour of SDN hardware and the Mininet emulator. The SDN hardware exposed higher latency and flow-setup time due to extra resources of delay, which the emulator does not possess

    Management of Distributed Software-Defined Networks in Smart Cities.

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    The emergence of smart city concept increases the needs for updating the abilities of the traditional network (Loffreda, 2015). The network of smart cities requires updating its topology and services dynamically using management software to automate this operation. In addition, it needs to run multiple logical networks over a single network infrastructure similarly to the data centres nowadays. The automation of dynamic behaviour achieves using a novel paradigm of the network that contains a forwarding devices have a central management, called Software-Define Network (SDN). SDN provides faster, cheaper and more efficient network. In addition, it strongly supports Network Function Virtualization (NFV), which enables executing the network functions using the software component instead of physical devices (Foundation, 2015). Therefore, NFV enables to run multiple logical networks on the single physical network. Moreover, it enables faster deployment of new services or updates the old ones.\nThe aim of the research is to develop a management algorithm automates the dynamical adaptation for network topology in smart cities

    Routing algorithm optimization for Software Defined Network WAN

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    Software Defined Network (SDN) provides a new fine-grained interface enables the routing algorithm to have a global view of the network throughputs, connectivity and flows at the data-path. This paper aims to provide a novel approach for dynamic routing algorithm for Software Defined Network in Wide Area Network (SDN-WAN); based on using a modified shortest-widest path algorithm with a fine-grained statistical method from the OpenFlow interface, called Shortest-Feasible OpenFlow Path (SFOP). This algorithm is designed to identify the optimal route from source to destination, providing efficient utilization of the SDN-WAN resources. It achieves this aim by considering both the flow requirements and the current state of the network. SFOP computes the optimal path which provides the feasible bandwidth with the lowest hop count (delay). That will present better stability in SDN communication, QoS, and usage of available resources. Moreover, this algorithm will be the base for an SDN controller because it extracts the widest available bandwidth from source to destination for a single path. It enables the controller to decide whether it is enough to use this simple algorithm only, or if a more complicated algorithm that provides larger bandwidth such as multiple-path algorithms is needed. Finally, a testbed has been implemented using MATLAB Simulator, Pox controller, and Mininet emulator will be discussed. The latency comparison of SFOP algorithm with three other algorithm’s latencies shows that this algorithm finds better latency for an optimal path. Evidence will be shown that demonstrates that SFOP has good stability in dynamic changes of SDN-WAN

    The management of the future internet

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    The future internet will include a cloud of assisted living and smart applications that serve a user by providing a remote communication and management for specific resources. It will contain a numerous number of fixed and movable devices, sensors, and actuators. This requires very fast and dynamic communication, which is performed by a novel paradigm of network that contains a forwarding device with a central management, called Software-Define Network (SDN). SDN provides a faster, cheaper and more efficient network. In addition, it strongly supports Network Function Virtualization (NFV), which enables the quicker development of the network by using the software programs for executing the network functions instead of physical devices. The aim of the research is to optimise the distribution and use of the SDN network resources through the following objectives: Develop the algorithm responsible for determining the initial number and the location of the elements of SDN network; The algorithm will extend to dynamically adapt this number and these locations according to the network changes; Model software platform to run SDN in wide Area Network and optimize its performance by applying the developed algorithms. The implications of SDN will be pervasive because it is a powerful network paradigm, which carries the solutions for today's problems. The proposed algorithm will fill part of the gap of the SDN network management and enhance its performance. This work aims to be a helpful tool to design, test and manage any SDN network, starting from the campus network and extending to multiple campuses or the city

    Evaluating Healthcare System Based SD-WAN Backbone

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    The development of information technology in the last few years led healthcare services to becomes more dependent on it, but the complexity of the current network is more susceptible to failure and causes interruption of the health services. SDN brought larger flexibility for network providers and enabled an improvement of the user’s experience for diverse data network services. Correspondingly, SDN delivered the flexibility for health care providers and improved the health state of their users. The aim of this paper is building a reliable SDN, optimizing controller application, building custom topology, test methodology, results from analysis ,to choosing the most suitable windows size and buffer size for the transfer medical data of SDN and give advice to researcher and Companies when build real SDN operating hospital. The major objective was to evaluate the Healthcare system over SD-WAN. This was applied by measuring the communication and the bandwidth between nodes; in addition to this we measured the latency, RTT and jitter for TCP and UDP protocols with Background traffic which variety from 20% to 70%using iperf, jperf and capturing in wireshark. The results show a noticeable diversity in different protocols. The carried-out investigation reveal that the performance of TCP is more reliable and suited for the transfer of medical data with the conclusion that background traffic and windows size in TCP have an inverse relationship. UDP Functions the best at a buffer size of 10, 30 KBin order to transmit medical data on real time with low packet loss

    Electrochemical defluorination of water: an experimental and morphological study

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    This experimental study concerns the elimination of fluoride from water using an electrocoagulation reactor having a variable flow direction in favour of increasing the electrolysing time, saving the reactor area, and water mixing. The detention time of the space-saver EC reactor (S-SECR) was measured and compared to the traditional reactors using an inert dye (red drain dye). Then, the influence of electrical current (1.5 ≀ ÎŽ ≀ 3.5 mA cm−2), pH of water (4 ≀ pH ≀ 10), and distance between electrodes (5 ≀ ϕ ≀ 15) on the defluoridation of water was analysed. The effect of the electrolysing activity on the electrodes' morphology was studied using scanning electron microscopy (SEM). Additionally, the operational cost was calculated. The results confirmed the removal of fluoride using S-SECR met the guideline of the World Health Organization (WHO) for fluoride levels in drinking water of ≀1.5 mg/L. S-SECR abated fluoride concentration from 20 mg/L to the WHO's guideline at ÎŽ, ϕ, pH, operational cost, and power consumption of 2.5 mA cm−2, 5 mm, 7, 0.346 USD m−3, and 5.03 kWh m−3, respectively. It was also found the S-SECR enhanced the detention time by 190% compared to the traditional reactors. The appearance of dents and irregularities on the surface of anodes in the SEM images proves the electrolysing process.Validerad;2022;NivĂ„ 2;2022-04-29 (sofila)</p

    ÙŰ§ŰčÙ„ÙŠŰ© ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ± في Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„ŰŁŰ­ÙŠŰ§ŰŠÙŠŰ© Ù„ŰŻÙ‰ Ű·Ù„Ű§Űš Ű§Ù„Ű”Ù Ű§Ù„Ű«Ű§Ù†ÙŠ Ű§Ù„Ù…ŰȘÙˆŰłŰ· ÙˆŰ§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ© Ù„ŰŻÙŠÙ‡Ù… The Effectiveness of The Model to Accelerate Thinking of Acquiring the Biological Concepts of the Second Grad Students average and the Mental Skills

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    The aim of the research to identify the effectiveness of the model to accelerate thinking in: 1. Acquire biological concepts among second-grade students in biology. 2.the mental skills of students in the second grade intermediate in biology. In order to verify the objectives of the research, the following two hypotheses were formulated: There are no statistical differences at the level of significance (0.05) between the average score of experimental students who study biology according to the model of acceleration of thinking and the average grades of control group students who study the same subject according to the usual method of testing the acquisition of concepts. 2. There are no significant differences at the level of significance (0.05) between the average score of students in the experimental group who study biology on the model of accelerating the thinking and the average grades of control group students who study the same article according to the usual method of mental skills. In order to verify the validity of the two hypotheses, a two-month trial was conducted. The following procedures were adopted: The experimental design of the experimental groups and the post-test controls was used to acquire the biological concepts and the mental skills. According to this design, the "Medium Banner of Islam for Boys" was chosen by the General Directorate of Education in the holy governorate of Karbala by the mean method. The school (106) students divided into three divisions (A - B - C), randomly selected (A), the number of students (35) students to represent the experimental group, who studied on the model acceleration of thinking, and in the same way was chosen Division (C) The number of students (34) students Such as the control group who studied according to the normal method. The two groups were then statistically compensated for a set of variables: the age of time calculated in months, the educational achievement of the parents, previous achievement in biology, previous information, intelligence. The scientific material was determined in the last three grades (seventh, eighth and ninth) of the biology book (2016, i7), which is to be taught for the second intermediate grade by the Iraqi Ministry of Education for the academic year 2016-2017. The content of the chapters was analyzed and a number of concepts (37) main concepts and (23) sub-concepts. According to these concepts, a number of behavioral goals were formulated, reaching (164) behavioral goals. In accordance with these objectives, (16) daily teaching plan for the experimental group and (16) p Of teaching daily to the control group. The researcher has developed according to conceptual map (15) a main and branch concept and gave each concept three experimental paragraphs according to the three cognitive processes (definition - discrimination - application) The test clauses were 45 multi-choice test pieces with four alternatives. The apparent honesty, the validity of the content (construction), the coefficient of difficulty, the coefficient of ease, the coefficient of discrimination, the effectiveness of the wrong alternatives for each of the test paragraphs were found, and the stability coefficient of the test was found in two ways: (0.85), corrected by Spearman-Brown (0.92), and Kyoder Richardson-20 (0.83) The second tool consisted of testing the mental skills, which consisted of (9) skills, each skill (4) test paragraphs, and thus the total number of test paragraphs (36) test paragraph of the type of multiple choice of four alternatives, The reliability of the content, the coefficient of difficulty, the coefficient of ease, the coefficient of discrimination, the effectiveness of the wrong alternatives and the stability coefficient were two ways: the midterm split (77%) and the Spearman-Brown equation (0.86) and the Kieder Richardson-20 equation (0.84(. The experiment was applied in the second semester of the academic year (2016 - 2017) and over the course of (8 weeks). The actual teaching started on Wednesday, 1/3/2017 and ended on Sunday, 2017). After using the appropriate statistical means, the results showed that the students of the experimental group who studied on the model of acceleration of thinking on the students of the control group studied according to the normal method in the tests of the acquisition of biological concepts and mental skills. In the teaching of biology for the second grade average because of the P In addition to suggesting a similar study on other variables and for other stages of study. Ù‡ŰŻÙ Ű§Ù„ŰšŰ­Ű« Ű„Ù„Ù‰ Ű§Ù„ŰȘŰčŰ±Ù Űčلى ÙŰ§ŰčÙ„ÙŠŰ© ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ± في : 1- Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„ŰŁŰ­ÙŠŰ§ŰŠÙŠŰ© Ù„ŰŻÙ‰ Ű·Ù„Ű§Űš Ű§Ù„Ű”Ù Ű§Ù„Ű«Ű§Ù†ÙŠ Ű§Ù„Ù…ŰȘÙˆŰłŰ· في Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ. 2- Ű§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ© Ù„ŰŻÙ‰ Ű·Ù„Ű§Űš Ű§Ù„Ű”Ù Ű§Ù„Ű«Ű§Ù†ÙŠ Ű§Ù„Ù…ŰȘÙˆŰłŰ· في Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ. ولŰș۱۶ Ű§Ù„ŰȘŰ­Ù‚ÙÙ‚ من Ù‡ŰŻÙÙŠ Ű§Ù„ŰšŰ­Ű« Ű”ÙŠŰșŰȘْ Ű§Ù„ÙŰ±Ű¶ÙŠŰȘŰ§Ù† Ű§Ù„Ű”ÙŰ±ÙŠŰȘŰ§Ù† Ű§Ù„ŰąŰȘيŰȘŰ§Ù† : 1- Ù„Ű§ ŰȘÙˆŰŹŰŻ ÙŰ±ÙˆÙ‚ ۰ۧŰȘ ŰŻÙ„Ű§Ù„Ű© Ű§Ű­Ű”Ű§ŰŠÙŠŰ© ŰčÙ†ŰŻ Ù…ŰłŰȘوى ŰŻÙ„Ű§Ù„Ű© (0.05) ŰšÙŠÙ† مŰȘÙˆŰłŰ· ۯ۱ۏۧŰȘ Ű·Ù„Ű§Űš Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ© Ű§Ù„Ű°ÙŠÙ† ÙŠŰŻŰ±ŰłÙˆÙ† Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ Űčلى وفق ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ± ÙˆŰšÙŠÙ† مŰȘÙˆŰłŰ· ۯ۱ۏۧŰȘ Ű·Ù„Ű§Űš Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„Ű¶Ű§ŰšŰ·Ű© Ű§Ù„Ű°ÙŠÙ† ÙŠŰŻŰ±ŰłÙˆÙ† Ű§Ù„Ù…Ű§ŰŻŰ© Ù†ÙŰłÙ‡Ű§ Űčلى وفق Ű§Ù„Ű·Ű±ÙŠÙ‚Ű© Ű§Ù„Ű§ŰčŰȘÙŠŰ§ŰŻÙŠŰ© في ۧ۟ŰȘۚۧ۱ Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ…. 2- Ù„Ű§ ŰȘÙˆŰŹŰŻ ÙŰ±ÙˆÙ‚ ۰ۧŰȘ ŰŻÙ„Ű§Ù„Ű© Ű§Ű­Ű”Ű§ŰŠÙŠŰ© ŰčÙ†ŰŻ Ù…ŰłŰȘوى ŰŻÙ„Ű§Ù„Ű© (0.05) ŰšÙŠÙ† مŰȘÙˆŰłŰ· ۯ۱ۏۧŰȘ Ű·Ù„Ű§Űš Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ© Ű§Ù„Ű°ÙŠÙ† ÙŠŰŻŰ±ŰłÙˆÙ† Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ Űčلى وفق ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ± ÙˆŰšÙŠÙ† مŰȘÙˆŰłŰ· ۯ۱ۏۧŰȘ Ű·Ù„Ű§Űš Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„Ű¶Ű§ŰšŰ·Ű© Ű§Ù„Ű°ÙŠÙ† ÙŠŰŻŰ±ŰłÙˆÙ† Ű§Ù„Ù…Ű§ŰŻŰ© Ù†ÙŰłÙ‡Ű§ Űčلى وفق Ű§Ù„Ű·Ű±ÙŠÙ‚Ű© Ű§Ù„Ű§ŰčŰȘÙŠŰ§ŰŻÙŠŰ© في Ű§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ©. وللŰȘŰ­Ù‚Ù‚ من ۔ۭ۩ Ű§Ù„ÙŰ±Ű¶ÙŠŰȘين Ű§Ù„Ű”ÙŰ±ÙŠŰȘين ŰŁÙŰŹŰ±ÙŠŰȘ ŰȘۏ۱ۚ۩ ۧ۳ŰȘŰșŰ±Ù‚ŰȘ ŰŽÙ‡Ű±ÙŠÙ† ÙƒŰ§Ù…Ù„ÙŠÙ†, Ű„Ű° ŰȘمَ ۧŰčŰȘÙ…Ű§ŰŻ Ű§Ù„Ű„ŰŹŰ±Ű§ŰĄŰ§ŰȘ Ű§Ù„ŰąŰȘÙŠŰ© : ۧ۳ŰȘŰźŰŻÙ… Ű§Ù„ŰȘŰ”Ù…ÙŠÙ… Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠ ۰ۧ Ű§Ù„Ű¶ŰšŰ· Ű§Ù„ŰŹŰČŰŠÙŠ Ù„Ù„Ù…ŰŹÙ…ÙˆŰčŰȘين Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ© ÙˆŰ§Ù„Ű¶Ű§ŰšŰ·Ű© Ű°ÙˆŰ§ŰȘ Ű§Ù„Ű§ŰźŰȘۚۧ۱ Ű§Ù„ŰšŰčŰŻÙŠ Ù„Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„ŰŁŰ­ÙŠŰ§ŰŠÙŠŰ© ÙˆŰ§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ©, وŰčلى وفق Ù‡Ű°Ű§ Ű§Ù„ŰȘŰ”Ù…ÙŠÙ… ŰȘمَ ۧ۟ŰȘÙŠŰ§Ű± (مŰȘÙˆŰłŰ·Ű© Ű±Ű§ÙŠŰ© Ű§Ù„Ű„ŰłÙ„Ű§Ù… Ù„Ù„ŰšÙ†ÙŠÙ†) Ű§Ù„ŰȘۧۚŰčŰ© Ű§Ù„Ù‰ Ű§Ù„Ù…ŰŻÙŠŰ±ÙŠŰ© Ű§Ù„ŰčŰ§Ù…Ű© للŰȘŰ±ŰšÙŠŰ© في Ù…Ű­Ű§ÙŰžŰ© ÙƒŰ±ŰšÙ„Ű§ŰĄ Ű§Ù„Ù…Ù‚ŰŻŰłŰ© ŰšŰ§Ù„Ű·Ű±ÙŠÙ‚Ű© Ű§Ù„Ù‚Ű”ŰŻÙŠŰ©, Ű„Ű° ŰšÙ„Űș ŰčŰŻŰŻ Ű·Ù„Ű§Űš Ű§Ù„Ű”Ù Ű§Ù„Ű«Ű§Ù†ÙŠ Ű§Ù„Ù…ŰȘÙˆŰłŰ· في Ű§Ù„Ù…ŰŻŰ±ŰłŰ© (106) Ű·Ù„Ű§Űš موŰČŰčين Űčلى Ű«Ù„Ű§Ű« ŰŽŰčŰš (ŰŁ - Űš - ŰŹ), ۧ۟ŰȘÙŠŰ±ŰȘ ŰŽŰčۚ۩ (ŰŁ) ŰčŰŽÙˆŰ§ŰŠÙŠŰ§Ù‹ ÙˆŰ§Ù„ŰšŰ§Ù„Űș ŰčŰŻŰŻ Ű·Ù„Ű§ŰšÙ‡Ű§ (35) Ű·Ű§Ù„ŰšŰ§Ù‹ لŰȘÙ…Ű«Ù„ Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ©, Ű§Ù„Ű°ÙŠÙ† ŰŻŰ±ŰłÙˆŰ§ Űčلى وفق ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ±, ÙˆŰšŰ§Ù„Ű·Ű±ÙŠÙ‚Ű© Ù†ÙŰłÙ‡Ű§ ۧ۟ŰȘÙŠŰ±ŰȘ ŰŽŰčۚ۩ (ŰŹ) ÙˆŰ§Ù„ŰšŰ§Ù„Űș ŰčŰŻŰŻ Ű·Ù„Ű§ŰšÙ‡Ű§ (34) Ű·Ű§Ù„ŰšŰ§Ù‹ لŰȘÙ…Ű«Ù„ Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„Ű¶Ű§ŰšŰ·Ű© Ű§Ù„Ű°ÙŠÙ† ŰŻŰ±ŰłÙˆŰ§ Űčلى وفق Ű§Ù„Ű·Ű±ÙŠÙ‚Ű© Ű§Ù„Ű§ŰčŰȘÙŠŰ§ŰŻÙŠŰ©, Ű«Ù… ÙƒÙˆÙŰŠŰȘ Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰȘŰ§Ù† Ű§Ű­Ű”Ű§ŰŠÙŠŰ§Ù‹ في Ù…ŰŹÙ…ÙˆŰčŰ© من Ű§Ù„Ù…ŰȘŰșÙŠŰ±Ű§ŰȘ هي : Ű§Ù„ŰčÙ…Ű± Ű§Ù„ŰČمني Ù…Ű­ŰłÙˆŰšŰ§Ù‹ ŰšŰ§Ù„ŰŽÙ‡ÙˆŰ±, Ű§Ù„ŰȘŰ­Ű”ÙŠÙ„ Ű§Ù„ŰŻŰ±Ű§ŰłÙŠ Ù„Ù„ÙˆŰ§Ù„ŰŻÙŠÙ†, Ű§Ù„ŰȘŰ­Ű”ÙŠÙ„ Ű§Ù„ŰłŰ§ŰšÙ‚ في Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ, Ű§Ù„Ù…ŰčÙ„ÙˆÙ…Ű§ŰȘ Ű§Ù„ŰłŰ§ŰšÙ‚Ű©, Ű§Ù„Ű°ÙƒŰ§ŰĄ. ÙˆÙ‚ŰŻ ŰȘŰ­ŰŻŰŻŰȘ Ű§Ù„Ù…Ű§ŰŻŰ© Ű§Ù„ŰčÙ„Ù…ÙŠŰ© ŰšŰ§Ù„ÙŰ”ÙˆÙ„ Ű§Ù„Ű«Ù„Ű§Ű«Ű© Ű§Ù„ŰŁŰźÙŠŰ±Ű© (Ű§Ù„ŰłŰ§ŰšŰč ÙˆŰ§Ù„Ű«Ű§Ù…Ù† ÙˆŰ§Ù„ŰȘۧ۳Űč) من كŰȘۧۚ Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ (2016,Ű·7) Ű§Ù„Ù…Ù‚Ű±Ű± ŰȘŰŻŰ±ÙŠŰłÙ‡ Ù„Ù„Ű”Ù Ű§Ù„Ű«Ű§Ù†ÙŠ Ű§Ù„Ù…ŰȘÙˆŰłŰ· من Ù‚ŰšÙ„ وŰČۧ۱۩ Ű§Ù„ŰȘŰ±ŰšÙŠŰ© Ű§Ù„ŰčŰ±Ű§Ù‚ÙŠŰ© للŰčŰ§Ù… Ű§Ù„ŰŻŰ±Ű§ŰłÙŠ (2016- 2017 م), ÙˆÙ‚ŰŻ ŰȘمَ ŰȘŰ­Ù„ÙŠÙ„ Ù…Ű­ŰȘوى Ű§Ù„ÙŰ”ÙˆÙ„ ÙˆŰ§ŰłŰȘ۟۱ۧۏ ŰčŰŻŰŻŰ§Ù‹ من Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„Ű±ŰŠÙŠŰłŰ© ÙˆŰ§Ù„ÙŰ±ŰčÙŠŰ©, Ű„Ű° ۧ۳ŰȘ۟۱ۏ (37) Ù…ÙÙ‡ÙˆÙ…Ű§Ù‹ Ű±ŰŠÙŠŰłŰ§Ù‹ و(23) Ù…ÙÙ‡ÙˆÙ…Ű§Ù‹ ÙŰ±ŰčÙŠŰ§Ù‹, وŰčلى وفق Ù‡Ű°Ù‡ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… ŰȘم Ű”ÙŠŰ§ŰșŰ© ŰčŰŻŰŻŰ§Ù‹ من Ű§Ù„ŰŁÙ‡ŰŻŰ§Ù Ű§Ù„ŰłÙ„ÙˆÙƒÙŠŰ© ŰšÙ„ŰșŰȘ ŰšŰ”ÙŠŰșŰȘÙ‡Ű§ Ű§Ù„Ù†Ù‡Ű§ŰŠÙŠŰ© (164) Ù‡ŰŻÙŰ§Ù‹ ŰłÙ„ÙˆÙƒÙŠŰ§Ù‹, وŰčلى وفق Ù‡Ű°Ù‡ Ű§Ù„ŰŁÙ‡ŰŻŰ§Ù ŰȘم Ű”ÙŠŰ§ŰșŰ© (32) ۟۷۩ ŰȘŰŻŰ±ÙŠŰłÙŠŰ© ÙŠÙˆÙ…ÙŠŰ© Ù„Ù„Ù…ŰŹÙ…ÙˆŰčŰȘين Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ© ÙˆŰ§Ù„Ű¶Ű§ŰšŰ·Ű©, ŰšÙ„ŰșŰȘ (16) ŰźŰ·Ű©Ù‹ ŰȘŰŻŰ±ÙŠŰłÙŠŰ©Ù‹ ÙŠÙˆÙ…ÙŠŰ©Ù‹ Ù„Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ© و(16) ۟۷۩ ŰȘŰŻŰ±ÙŠŰłÙŠŰ© ÙŠÙˆÙ…ÙŠŰ© Ù„Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„Ű¶Ű§ŰšŰ·Ű©. ÙˆÙ‚ŰŻ ŰȘم ۧŰčۯۧۯ ۣۯۧŰȘين Ù„Ù„ŰšŰ­Ű« Ù‡Ù…Ű§ ۧ۟ŰȘۚۧ۱ Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„ŰŁŰ­ÙŠŰ§ŰŠÙŠŰ© ŰšŰ§Ù„Ű§ŰčŰȘÙ…Ű§ŰŻ Űčلى Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„Ű±ŰŠÙŠŰłŰ© ÙˆŰ§Ù„ÙŰ±ŰčÙŠŰ©, Ű„Ű° Ű­ŰŻŰŻ Ű§Ù„ŰšŰ§Ű­Ű« Űčلى وفق Ű§Ù„ŰźŰ§Ű±Ű·Ű© Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ…ÙŠŰ© (15) Ù…ÙÙ‡ÙˆÙ…Ű§Ù‹ Ű±ŰŠÙŠŰłŰ§Ù‹ ÙˆÙŰ±ŰčÙŠŰ§Ù‹ ÙˆŰŁŰčŰ·Ù‰ لكل مفهوم Ű«Ù„Ű§Ű« ÙÙ‚Ű±Ű§ŰȘ ۧ۟ŰȘŰšŰ§Ű±ÙŠŰ© Űčلى وفق Ű§Ù„ŰčÙ…Ù„ÙŠŰ§ŰȘ Ű§Ù„Ù…ŰčŰ±ÙÙŠŰ© Ű§Ù„Ű«Ù„Ű§Ű«Ű© : (ŰȘŰčŰ±ÙŠÙ - ŰȘمييŰČ - ŰȘŰ·ŰšÙŠÙ‚) Ù„ÙŠŰ”ŰšŰ­ ŰčŰŻŰŻ ÙÙ‚Ű±Ű§ŰȘ Ű§Ù„Ű§ŰźŰȘۚۧ۱ (45) ÙÙ‚Ű±Ű© ۧ۟ŰȘŰšŰ§Ű±ÙŠŰ© من نوŰč Ű§Ù„Ű§ŰźŰȘÙŠŰ§Ű± من مŰȘŰčŰŻŰŻ Ű°ÙŠ ۣ۱ۚŰčŰ© ŰšŰŻŰ§ŰŠÙ„, وŰȘم Ű§ÙŠŰŹŰ§ŰŻ Ű§Ù„Ű”ŰŻÙ‚ Ű§Ù„ŰžŰ§Ù‡Ű±ÙŠ ÙˆŰ”ŰŻÙ‚ Ű§Ù„Ù…Ű­ŰȘوى (Ű§Ù„ŰšÙ†Ű§ŰĄ) ومŰčŰ§Ù…Ù„ Ű§Ù„Ű”ŰčÙˆŰšŰ© ومŰčŰ§Ù…Ù„ Ű§Ù„ŰłÙ‡ÙˆÙ„Ű© ومŰčŰ§Ù…Ù„ Ű§Ù„ŰȘمييŰČ ÙˆÙŰčŰ§Ù„ÙŠŰ© Ű§Ù„ŰšŰŻŰ§ŰŠÙ„ Ű§Ù„ŰźŰ§Ű·ŰŠŰ© لكل ÙÙ‚Ű±Ű© من ÙÙ‚Ű±Ű§ŰȘ Ű§Ù„Ű§ŰźŰȘۚۧ۱, ÙˆŰ§ÙŠŰŹŰ§ŰŻ مŰčŰ§Ù…Ù„ Ű§Ù„Ű«ŰšŰ§ŰȘ Ù„Ù„Ű§ŰźŰȘۚۧ۱ ŰšŰ·Ű±ÙŠÙ‚ŰȘين Ù‡Ù…Ű§ : Ű§Ù„ŰȘŰŹŰČŰŠŰ© Ű§Ù„Ù†Ű”ÙÙŠŰ© ÙˆŰ§Ù„Ű°ÙŠ ŰšÙ„Űș (0.85) وŰčÙ†ŰŻ ŰȘŰ”Ű­ÙŠŰ­Ù‡ ŰšÙ…ŰčŰ§ŰŻÙ„Ű© ŰłŰšÙŠŰ±Ù…Ű§Ù† – ŰšŰ±Ű§ÙˆÙ† ŰšÙ„Űș (0.92), ومŰčŰ§ŰŻÙ„Ű© ÙƒÙŠÙˆŰŻŰ± Ű±ÙŠŰȘŰŽŰ§Ű±ŰŻŰłÙˆÙ†- 20 ÙˆŰ§Ù„Ű°ÙŠ ŰšÙ„Űș (0.83) ŰŁÙ…Ű§ ŰšŰ§Ù„Ù†ŰłŰšŰ© Ù„Ù„ŰŁŰŻŰ§Ű© Ű§Ù„Ű«Ű§Ù†ÙŠŰ© فŰȘÙ…Ű«Ù„ŰȘ ۚۧ۟ŰȘۚۧ۱ Ű§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ© Ű§Ù„Ű°ÙŠ ŰȘكون من (9) Ù…Ù‡Ű§Ű±Ű§ŰȘ, لكل Ù…Ù‡Ű§Ű±Ű© (4) ÙÙ‚Ű±Ű§ŰȘ ۧ۟ŰȘŰšŰ§Ű±ÙŠŰ©, ÙˆŰšŰ°Ù„Ùƒ ŰšÙ„Űș Ű§Ù„ŰčŰŻŰŻ Ű§Ù„ÙƒÙ„ÙŠ Ù„ÙÙ‚Ű±Ű§ŰȘ Ű§Ù„Ű§ŰźŰȘۚۧ۱ (36) ÙÙ‚Ű±Ű© ۧ۟ŰȘŰšŰ§Ű±ÙŠŰ© من نوŰč Ű§Ù„Ű§ŰźŰȘÙŠŰ§Ű± من مŰȘŰčŰŻŰŻ Ű°ÙŠ ۣ۱ۚŰčŰ© ŰšŰŻŰ§ŰŠÙ„, ÙˆŰ­ÙŽŰłÙŽŰšÙŽ Ű§Ù„ŰšŰ§Ű­Ű« ŰŁÙŠŰ¶ŰŁÙ‹ Ű§Ù„Ű”ŰŻÙ‚ Ű§Ù„ŰžŰ§Ù‡Ű±ÙŠ, ÙˆŰ”ŰŻÙ‚ Ű§Ù„Ù…Ű­ŰȘوى, ومŰčŰ§Ù…Ù„ Ű§Ù„Ű”ŰčÙˆŰšŰ©, ومŰčŰ§Ù…Ù„ Ű§Ù„ŰłÙ‡ÙˆÙ„Ű©, ومŰčŰ§Ù…Ù„ Ű§Ù„ŰȘمييŰČ, وفŰčŰ§Ù„ÙŠŰ© Ű§Ù„ŰšŰŻŰ§ŰŠÙ„ Ű§Ù„ŰźŰ§Ű·ŰŠŰ© ومŰčŰ§Ù…Ù„ Ű§Ù„Ű«ŰšŰ§ŰȘ ŰšŰ·Ű±ÙŠÙ‚ŰȘين Ù‡Ù…Ű§ : Ű§Ù„ŰȘŰŹŰČŰŠŰ© Ű§Ù„Ù†Ű”ÙÙŠŰ© ÙˆŰ§Ù„Ű°ÙŠ ŰšÙ„Űș (77%) وŰčÙ†ŰŻ ŰȘŰ”Ű­ÙŠŰ­Ù‡ ŰšÙ…ŰčŰ§ŰŻÙ„Ű© ŰłŰšÙŠŰ±Ù…Ű§Ù† - ŰšŰ±Ű§ÙˆÙ† ŰšÙ„Űș (0.86), ومŰčŰ§ŰŻÙ„Ű© ÙƒÙŠÙˆŰŻŰ± Ű±ÙŠŰȘŰŽŰ§Ű±ŰŻŰłÙˆÙ†- 20 ÙˆŰ§Ù„Ű°ÙŠ ŰšÙ„Űș (0.84). Ű·ÙŰšÙÙ‚ÙŽŰȘ Ű§Ù„ŰȘۏ۱ۚ۩ في Ű§Ù„ÙŰ”Ù„ Ű§Ù„ŰŻŰ±Ű§ŰłÙŠ Ű§Ù„Ű«Ű§Ù†ÙŠ للŰčŰ§Ù… Ű§Ù„ŰŻŰ±Ű§ŰłÙŠ (2016- 2017 م) وŰčلى Ù…ŰŻÙ‰ (8 ŰŁŰłŰ§ŰšÙŠŰč), Ű„Ű° ÙƒŰ§Ù†ŰȘ ŰšŰŻŰ§ÙŠŰ© Ű§Ù„ŰȘŰŻŰ±ÙŠŰł Ű§Ù„ÙŰčلي يوم (Ű§Ù„ŰŁŰ±ŰšŰčۧۥ) Ű§Ù„Ù…ÙˆŰ§ÙÙ‚ (1/3/2017 م) ÙˆÙ†Ù‡Ű§ÙŠŰȘه يوم (Ű§Ù„ŰŁŰ­ŰŻ) Ű§Ù„Ù…ÙˆŰ§ÙÙ‚ (30/4/2017 م), ÙˆŰšŰčŰŻ ۧ۳ŰȘŰźŰŻŰ§Ù… Ű§Ù„ÙˆŰłŰ§ŰŠÙ„ Ű§Ù„Ű„Ű­Ű”Ű§ŰŠÙŠŰ© Ű§Ù„Ù…Ù†Ű§ŰłŰšŰ© ŰŁŰžÙ‡Ű±ŰȘ Ű§Ù„Ù†ŰȘۧۊۏ ŰȘفوق Ű·Ù„Ű§Űš Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„ŰȘŰŹŰ±ÙŠŰšÙŠŰ© Ű§Ù„Ű°ÙŠÙ† ŰŻŰ±ŰłÙˆŰ§ Űčلى وفق ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ± Űčلى Ű·Ù„Ű§Űš Ű§Ù„Ù…ŰŹÙ…ÙˆŰčŰ© Ű§Ù„Ű¶Ű§ŰšŰ·Ű© Ű§Ù„Ű°ÙŠÙ† ŰŻŰ±ŰłÙˆŰ§ Űčلى وفق Ű§Ù„Ű·Ű±ÙŠÙ‚Ű© Ű§Ù„Ű§ŰčŰȘÙŠŰ§ŰŻÙŠŰ© في ۧ۟ŰȘŰšŰ§Ű±ÙŠ Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„ŰŁŰ­ÙŠŰ§ŰŠÙŠŰ© ÙˆŰ§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ© , وفي Ű¶ÙˆŰĄ Ű§Ù„Ù†ŰȘۧۊۏ ŰŁÙˆŰ”Ù‰ Ű§Ù„ŰšŰ§Ű­Ű« ŰšÙ…Ű­Ű§ÙˆÙ„Ű© ۧŰčŰȘÙ…Ű§ŰŻ ŰŁÙ†Ù…ÙˆŰ°ŰŹ ŰȘŰłŰ±ÙŠŰč Ű§Ù„ŰȘÙÙƒÙŠŰ± في ŰȘŰŻŰ±ÙŠŰł Ù…Ű§ŰŻŰ© Űčلم Ű§Ù„ŰŁŰ­ÙŠŰ§ŰĄ Ù„Ù„Ű”Ù Ű§Ù„Ű«Ű§Ù†ÙŠ Ű§Ù„Ù…ŰȘÙˆŰłŰ· Ù„Ù…Ű§ له من ÙŰ§ŰčÙ„ÙŠŰ© في Ű±ÙŰč Ù…ŰłŰȘوى Ű§ÙƒŰȘ۳ۧۚ Ű§Ù„Ù…ÙŰ§Ù‡ÙŠÙ… Ű§Ù„ŰŁŰ­ÙŠŰ§ŰŠÙŠŰ© ÙˆŰ§Ù„Ù…Ù‡Ű§Ű±Ű§ŰȘ Ű§Ù„ŰčÙ‚Ù„ÙŠŰ© Ù„Ù„Ű·Ù„Ű§Űš, ÙˆÙƒŰ°Ù„Ùƒ Ű§Ù‚ŰȘ۱ۭ ۄۏ۱ۧۥ ۯ۱ۧ۳۩ Ù…Ù…Ű§Ű«Ù„Ű© Űčلى مŰȘŰșÙŠŰ±Ű§ŰȘ ŰŁÙŰźŰ± ÙˆÙ„Ù…Ű±Ű§Ű­Ù„ ŰŻŰ±Ű§ŰłÙŠŰ© ŰŁÙŰźŰ±

    SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

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    Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population
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