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Статті в журналах з теми "Networks and systems"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 4 липня 2023

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1

Anno, Junpei, Leonard Barolli, Arjan Durresi, Fatos Xhafa, and Akio Koyama. "Performance Evaluation of Two Fuzzy-Based Cluster Head Selection Systems for Wireless Sensor Networks." Mobile Information Systems 4, no. 4 (2008): 297–312. http://dx.doi.org/10.1155/2008/876212.

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Sensor networks supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities are emerging as a critically important computer class that enable novel and low cost applications. There are many fundamental problems that sensor networks research will have to address in order to ensure a reasonable degree of cost and system quality. Cluster formation and cluster head selection are important problems in sensor network applications and can drastically affect the network's communication energy dissipation. However, selecting of the cluster head is not easy in different environments which may have different characteristics. In this paper, in order to deal with this problem, we propose two fuzzy-based systems for cluster head selection in sensor networks. We call these systems: FCHS System1 and FCHS System2. We evaluate the proposed systems by simulations and have shown that FCHS System2 make a good selection of the cluster head compared with FCHS System1 and another previous system.

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2

Best, Allan, Alex Berland, Trisha Greenhalgh, Ivy L. Bourgeault, Jessie E. Saul, and Brittany Barker. "Networks as systems." Journal of Health Organization and Management 32, no. 1 (March 19, 2018): 9–24. http://dx.doi.org/10.1108/jhom-06-2017-0129.

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Purpose The purpose of this paper is to present a case study of the World Health Organization’s Global Healthcare Workforce Alliance (GHWA). Based on a commissioned evaluation of GHWA, it applies network theory and key concepts from systems thinking to explore network emergence, effectiveness, and evolution to over a ten-year period. The research was designed to provide high-level strategic guidance for further evolution of global governance in human resources for health (HRH). Design/methodology/approach Methods included a review of published literature on HRH governance and current practice in the field and an in-depth case study whose main data sources were relevant GHWA background documents and key informant interviews with GHWA leaders, staff, and stakeholders. Sampling was purposive and at a senior level, focusing on board members, executive directors, funders, and academics. Data were analyzed thematically with reference to systems theory and Shiffman’s theory of network development. Findings Five key lessons emerged: effective management and leadership are critical; networks need to balance “tight” and “loose” approaches to their structure and processes; an active communication strategy is key to create and maintain support; the goals, priorities, and membership must be carefully focused; and the network needs to support shared measurement of progress on agreed-upon goals. Shiffman’s middle-range network theory is a useful tool when guided by the principles of complex systems that illuminate dynamic situations and shifting interests as global alliances evolve. Research limitations/implications This study was implemented at the end of the ten-year funding cycle. A more continuous evaluation throughout the term would have provided richer understanding of issues. Experience and perspectives at the country level were not assessed. Practical implications Design and management of large, complex networks requires ongoing attention to key issues like leadership, and flexible structures and processes to accommodate the dynamic reality of these networks. Originality/value This case study builds on growing interest in the role of networks to foster large-scale change. The particular value rests on the longitudinal perspective on the evolution of a large, complex global network, and the use of theory to guide understanding.

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3

Kurths, J., D. Maraun, C. S. Zhou, G. Zamora-Lopez, and Y. Zou. "Dynamics in Complex Systems." European Review 17, no. 2 (May 2009): 357–70. http://dx.doi.org/10.1017/s1062798709000726.

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Over the last decade, we have witnessed the birth of a new movement of interest and research in the study of complex networks. These networks often have irregular structural properties, but also encompass rich dynamics. The interplay between the network topological structure and the associated dynamics attracts a lot of interest. In this research line, we propose a network approach to dealing with complex dynamics, in particular with synchronization dynamics. From the methodological perspective, this approach requires novel ideas from nonlinear sciences, statistical physics and mathematical statistics. Furthermore, we show applications in different disciplines, from earth sciences to brain dynamics. The complex network’s approach is an interdisciplinary topic and could be promising for the understanding of complexity from a systems level.

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4

Veselý, A., and D. Brechlerová. "Neural networks in intrusion detection systems." Agricultural Economics (Zemědělská ekonomika) 50, No. 1 (February 24, 2012): 35–40. http://dx.doi.org/10.17221/5164-agricecon.

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Security of an information system is its very important property, especially today, when computers are interconnected via internet. Because no system can be absolutely secure, the timely and accurate detection of intrusions is necessary. For this purpose, Intrusion Detection Systems (IDS) were designed. There are two basic models of IDS: misuse IDS and anomaly IDS. Misuse systems detect intrusions by looking for activity that corresponds to the known signatures of intrusions or vulnerabilities. Anomaly systems detect intrusions by searching for an abnormal system activity. Most IDS commercial tools are misuse systems with rule-based expert system structure. However, these techniques are less successful when attack characteristics vary from built-in signatures. Artificial neural networks offer the potential to resolve these problems. As far as anomaly systems are concerned, it is very difficult to build them, because it is difficult to define the normal and abnormal behaviour of a system. Also for building anomaly system, neural networks can be used, because they can learn to discriminate the normal and abnormal behaviour of a system from examples. Therefore, they offer a promising technique for building anomaly systems. This paper presents an overview of the applicability of neural networks in building intrusion systems and discusses advantages and drawbacks of neural network technology.

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5

Bottoni, Paolo, Anna Labella, and Grzegorz Rozenberg. "Networks of Reaction Systems." International Journal of Foundations of Computer Science 31, no. 01 (January 2020): 53–71. http://dx.doi.org/10.1142/s0129054120400043.

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In this paper, we study the behavior (processes) of reaction systems where the context is not arbitrary, but it has its own structure. In particular, we consider a model where the context for a reaction system originates from a network of reaction systems. Such a network is formalized as a graph with reaction systems residing at its nodes, where each reaction system contributes to defining the context of all its neighbors. This paper provides a framework for investigating the behavior of reaction systems receiving contexts from networks of reaction systems, provides a characterisation of their state sequences, and considers different topologies of context networks.

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6

Gadasin, Denis V., Andrey V. Shvedov, and Alyona A. Yudin. "CLUSTERING METHODS IN LARGE-SCALE SYSTEMS." SYNCHROINFO JOURNAL 6, no. 5 (2020): 21–24. http://dx.doi.org/10.36724/2664-066x-2020-6-5-21-24.

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Interactions between people, groups, organizations, and biological cells have a relationship character that can be represented as a network. The system properties of such networks, regardless of their physical nature, but clearly determining the performance of networks, create the totality of the real world. Complex networks – are naturally existing networks (graphs) that have complex topological properties. The researchers who participate and also make discoveries in this field come from various Sciences such as mathematics, computer science, physics, sociology, and engineering. Therefore, the results of research carry both theoretical knowledge and practical applications in these Sciences. This paper discusses the definition of complex networks. The main characteristics of complex networks, such as clustering and congestion, are considered. A popular social network is considered as a complex network. The calculation of nodes and links of the considered social network is made. The main types of AI development and training are highlighted.

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7

RIDER, ANDREW K., TIJANA MILENKOVIĆ, GEOFFREY H. SIWO, RICHARD S. PINAPATI, SCOTT J. EMRICH, MICHAEL T. FERDIG, and NITESH V. CHAWLA. "Networks' characteristics are important for systems biology." Network Science 2, no. 2 (August 2014): 139–61. http://dx.doi.org/10.1017/nws.2014.13.

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AbstractA fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations.Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.

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8

Vishvakarma, Tanmya. "A Review of Modern Computer Networks." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 368–76. http://dx.doi.org/10.22214/ijraset.2022.46637.

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Abstract: A computer network's principal role is to facilitate the transfer of digital information between computers. An interconnected network of computer equipment is all that constitutes a computer network. Distributing tasks among different machines is frequent in a computer network. Most networks confront substantial threats from attacks on their resources. This research focuses on computer networks. It is only through the establishment of a computer network that this is made possible at all. Nodes include things like computers, cell phones, servers, and other networking gear. Computer networks enable the Internet, as well as the shared use of application and storage servers, printers, and fax machines. Through the use of computer networks, many jobs can be completed. Systems can be classified as open or closed. If you want to connect to the network, the open system is ready to go. Closed systems, on the other hand, require further authentication to connect to other networks.

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9

He, Liang, and Shouwei Li. "Network Entropy and Systemic Risk in Dynamic Banking Systems." Complexity 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/1852897.

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We investigate network entropy of dynamic banking systems, where interbank networks analyzed include random networks, small-world networks, and scale-free networks. We find that network entropy is positively correlated with the effect of systemic risk in the three kinds of interbank networks and that network entropy in the small-world network is the largest, followed by those in the random network and the scale-free network.

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10

Jalili, Mahdi. "Network biology: Describing biological systems by complex networks." Physics of Life Reviews 24 (March 2018): 159–61. http://dx.doi.org/10.1016/j.plrev.2017.12.003.

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11

Polishchuk, O. D., and M. S. Yadzhak. "Network structures and systems: III. Hierarchies and networks." System research and information technologies, no. 4 (December 18, 2018): 82–95. http://dx.doi.org/10.20535/srit.2308-8893.2018.4.07.

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12

Virgil Negoita, Constantin. "Neural Networks as Fuzzy Systems." Kybernetes 23, no. 3 (April 1, 1994): 7–9. http://dx.doi.org/10.1108/03684929410059000.

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Any fuzzy system is a knowledge‐based system which implies an inference engine. Proposes neural networks as a means of performing the inference. Using the Theorem of Representation proposes an encoding scheme that allows the neural network to be trained to perform modus ponens.

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13

Bunimovich, Leonid, Dallas Smith, and Benjamin Webb. "Finding Hidden Structures, Hierarchies, and Cores in Networks via Isospectral Reduction." Applied Mathematics and Nonlinear Sciences 4, no. 1 (June 28, 2019): 231–54. http://dx.doi.org/10.2478/amns.2019.1.00021.

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AbstractThe method of isospectral network reduction allows one the ability to reduce a network while preserving the network’s spectral structure. In this paper we describe a number of recent applications of the theory of isospectral reductions. This includes finding hidden structures, specifically latent symmetries, in networks, uncovering different network hierarchies, and simultaneously determining different network cores. We also specify how such reductions can be interpreted as dynamical systems and describe the type of dynamics such systems have. Additionally, we show how the recent theory of equitable decompositions can be paired with the method of isospectral reductions to decompose networks.

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14

Calise, Santiago Gabriel. "Social Networks and Systems Theory." Sotsiologicheskoe Obozrenie / Russian Sociological Review 20, no. 1 (2021): 72–88. http://dx.doi.org/10.17323/1728-192x-2021-1-72-88.

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This paper provides a critical assessment of the conceptions of social networks in systems theory. There are two main solutions to the problem: treating networks as systems, or denying them that status. The last group conceives networks as structural couplings (Kämper and Schmidt) or as forms (Fuhse). Among the first group, Luhmann has used the concept to describe the particularities of the societal structure of underdeveloped regions, but he did not delve into a theoretical characterization of the concept. Teubner’s version also remained associated with a particular episode, providing no general network theory. Bommes and Tacke establish reciprocity as the central mechanism, which relates different addresses (persons or organizations) through a non-specific future promise of a service in return for a favor. The analysis shows that this account provides the most complete version of the concept, remaining fully compatible with systems theory.

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15

Rani, Meenu, and Poonam Singal. "Networks of Underwater Sensor Wireless Systems." International Journal of Wireless Networks and Broadband Technologies 10, no. 1 (January 2021): 59–69. http://dx.doi.org/10.4018/ijwnbt.2021010104.

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Underwater wireless networks have been the subject of considerable attention in research and development by both academia and industry, while applications are expanding to a wide range of uses, including industrial, scientific, military, and environmental applications. The paper presents a analysis of the underwater wireless sensor network, a system that is promising to reveal the secrets of marine life and other underwater applications. The information about the underwater channel was listed with a focus on communication of both the acoustic and optical kind. Then, the node location strategies and related protocols for routing that can be applied to the desired communication type were discussed briefly. The hard environment and peculiar features of UWSNs are responsible for efficient communication between sensors in UWSNs. This paper proposes a robust and energy-efficient UWSN location-free routing system, based on constraint. RE-PBR takes into account three criteria, including performance, depth, and residual power connections, to balance energy consumption and to produce usable results. The findings of the simulation show that the proposed work decreases travel costs and by using less energy increases the network's life.

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16

Feltus, Christophe. "AI’S Contribution to Ubiquitous Systems and Pervasive Networks Security – Reinforcement Learning vs Recurrent Networks." Journal of Ubiquitous Systems and Pervasive Networks 15, no. 02 (March 1, 2021): 01–09. http://dx.doi.org/10.5383/juspn.15.02.001.

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Reinforcement learning and recurrent networks are two emerging machine-learning paradigms. The first learns the best actions an agent needs to perform to maximize its rewards in a particular environment and the second has the specificity to use an internal state to remember previous analysis results and consider them for the current one. Research into RL and recurrent network has been proven to have made a real contribution to the protection of ubiquitous systems and pervasive networks against intrusions and malwares. In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL and recurrent network-based research in network security was complete.

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17

Brokan, Eduard, and Felix Sadyrbaev. "Networks Describing Dynamical Systems." Tatra Mountains Mathematical Publications 71, no. 1 (December 1, 2018): 39–52. http://dx.doi.org/10.2478/tmmp-2018-0004.

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Abstract We consider systems of ordinary differential equations that arise in the theory of gene regulatory networks. These systems can be of arbitrary size but of definite structure that depends on the choice of regulatory matrices. Attractors play the decisive role in behaviour of elements of such systems. We study the structure of simple attractors that consist of a number of critical points for several choices of regulatory matrices.

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18

Malyshev, V. A. "Networks and dynamical systems." Advances in Applied Probability 25, no. 1 (March 1993): 140–75. http://dx.doi.org/10.2307/1427500.

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A new approach to the problem of classification of (deflected) random walks in or Markovian models for queueing networks with identical customers is introduced. It is based on the analysis of the intrinsic dynamical system associated with the random walk. Earlier results for small dimensions are presented from this novel point of view. We give proofs of new results for higher dimensions related to the existence of a continuous invariant measure for the underlying dynamical system. Two constants are shown to be important: the free energy M < 0 corresponds to ergodicity, the Lyapounov exponent L < 0 defines recurrence. General conjectures, examples, unsolved problems and surprising connections with ergodic theory, classical dynamical systems and their random perturbations are largely presented. A useful notion naturally arises, the so-called scaled random perturbation of a dynamical system.

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19

Malyshev, V. A. "Networks and dynamical systems." Advances in Applied Probability 25, no. 01 (March 1993): 140–75. http://dx.doi.org/10.1017/s0001867800025210.

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Анотація:

A new approach to the problem of classification of (deflected) random walks inor Markovian models for queueing networks with identical customers is introduced. It is based on the analysis of the intrinsic dynamical system associated with the random walk. Earlier results for small dimensions are presented from this novel point of view. We give proofs of new results for higher dimensions related to the existence of a continuous invariant measure for the underlying dynamical system. Two constants are shown to be important: the free energyM< 0 corresponds to ergodicity, the Lyapounov exponentL< 0 defines recurrence. General conjectures, examples, unsolved problems and surprising connections with ergodic theory, classical dynamical systems and their random perturbations are largely presented. A useful notion naturally arises, the so-called scaled random perturbation of a dynamical system.

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20

Lerman, Eugene. "Networks of open systems." Journal of Geometry and Physics 130 (August 2018): 81–112. http://dx.doi.org/10.1016/j.geomphys.2018.03.020.

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21

Evans, J., and Z. Zvonar. "Components, systems, and networks." IEEE Communications Magazine 44, no. 9 (September 2006): 42. http://dx.doi.org/10.1109/mcom.2006.1705973.

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22

Holzenkamp, Christa, Eric Sax, and Dirk Walliser. "Testing electronics systems networks." ATZelektronik worldwide 1, no. 4 (December 2006): 11–13. http://dx.doi.org/10.1007/bf03242098.

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23

Hübler, Alfred W. "Understanding complex systems: Networks." Complexity 10, no. 3 (January 2005): 17. http://dx.doi.org/10.1002/cplx.20072.

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24

Herrera, Manuel, Marco Pérez-Hernández, Ajith Kumar Parlikad, and Joaquín Izquierdo. "Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering." Processes 8, no. 3 (March 8, 2020): 312. http://dx.doi.org/10.3390/pr8030312.

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Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant.

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25

Peng, Hao, Cheng Qian, Dandan Zhao, Ming Zhong, Jianmin Han, and Wei Wang. "Targeting attack hypergraph networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073121. http://dx.doi.org/10.1063/5.0090626.

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In modern systems, from brain neural networks to social group networks, pairwise interactions are not sufficient to express higher-order relationships. The smallest unit of their internal function is not composed of a single functional node but results from multiple functional nodes acting together. Therefore, researchers adopt the hypergraph to describe complex systems. The targeted attack on random hypergraph networks is still a problem worthy of study. This work puts forward a theoretical framework to analyze the robustness of random hypergraph networks under the background of a targeted attack on nodes with high or low hyperdegrees. We discovered the process of cascading failures and the giant connected cluster (GCC) of the hypergraph network under targeted attack by associating the simple mapping of the factor graph with the hypergraph and using percolation theory and generating function. On random hypergraph networks, we do Monte-Carlo simulations and find that the theoretical findings match the simulation results. Similarly, targeted attacks are more effective than random failures in disintegrating random hypergraph networks. The threshold of the hypergraph network grows as the probability of high hyperdegree nodes being deleted increases, indicating that the network’s resilience becomes more fragile. When considering real-world scenarios, our conclusions are validated by real-world hypergraph networks. These findings will help us understand the impact of the hypergraph’s underlying structure on network resilience.

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26

Gavalas, Damianos, Modestos Stavrakis, Periklis Chatzimisios, Zhichao Cao, and Xiaolong Zheng. "Wireless Systems and Networks in the IoT." Sensors 20, no. 8 (April 17, 2020): 2279. http://dx.doi.org/10.3390/s20082279.

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This Special Issue is focused on breakthrough developments in the field of Wireless Systems and Networks in the IoT. The selected contributions report current scientific progress in a wide range of topics covering clock error compensation in sensor networks, backscatter communication networks, Radio-Frequency Identification (RFID)-based inventory management, resource allocation in Long-Term Evolution (LTE)/LTE-A, (Long Range Wide-Area Network (LoRaWAN) modeling and key generation for the IoT.

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27

Podroiko, Ye V., and Yu M. Lysetskyi. "Network technologies: evolution and peculiarities. Mathematical machines and systems." Mathematical machines and systems 2 (2020): 14–29. http://dx.doi.org/10.34121/1028-9763-2020-2-14-29.

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Today corporate network is seen as a complex system and traditionally provides the set of interacting essential components, such as: Main Site – a network of head office; Remote Site (Branch) – networks of remote office; WAN – global network uniting networks of the offices; LAN – a local network; WAN Edge – a point of connection to WAN. Internet Edge – a point of connection to the Internet; Data Cen-ter – corporate centre of data processing. Some sources also regard Service Block as a component, which is a separate segment of the network with specific services. Every component of corporate network fea-tures contains individual set of technologies, each having its history of origination and development. The paper offers short review of basic technologies which form the history of development of corporate network, as well as their evolution from a set of separated network technologies to a unified multi-service network infrastructure. This unified infrastructure is inextricably linked with a global network of Internet which is both a service and a carrier for majority of modern corporate networks. The paper de-scribes origination and development of Internet, local and global networks, Wi-Fi networks and software defined networks. Corporate network has been through a long evolution from co-existence of separated technologies to modern unified intellectual network infrastructure with high security and reliable man-agement. Due to fast-moving development of information technologies the corporate networks have dynamically transformed in several directions: network functions virtualization (NFV – Network Func-tions Virtualization); utilization of SDN solutions; automation of management processes; analytics; se-curity; cloud services. In the course of such a transformation the corporate network turned into unified, flexible, application oriented infrastructure with high reliability, easily modified and expanded function-ality, single management center, unified security policies, fast and detailed analysis of internal network processes.

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28

Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (April 20, 2020): 270. http://dx.doi.org/10.3390/ijgi9040270.

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Many real-world spatial systems can be conceptualized as networks. In these conceptualizations, nodes and links represent system components and their interactions, respectively. Traditional network analysis applies graph theory measures to static network datasets. However, recent interest lies in the representation and analysis of evolving networks. Existing network automata approaches simulate evolving network structures, but do not consider the representation of evolving networks embedded in geographic space nor integrating actual geospatial data. Therefore, the objective of this study is to integrate network automata with geographic information systems (GIS) to develop a novel modelling framework, Geographic Network Automata (GNA), for representing and analyzing complex dynamic spatial systems as evolving geospatial networks. The GNA framework is implemented and presented for two case studies including a spatial network representation of (1) Conway’s Game of Life model and (2) Schelling’s model of segregation. The simulated evolving spatial network structures are measured using graph theory. Obtained results demonstrate that the integration of concepts from geographic information science, complex systems, and network theory offers new means to represent and analyze complex spatial systems. The presented GNA modelling framework is both general and flexible, useful for modelling a variety of real geospatial phenomena and characterizing and exploring network structure, dynamics, and evolution of real spatial systems. The proposed GNA modelling framework fits within the larger framework of geographic automata systems (GAS) alongside cellular automata and agent-based modelling.

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29

Rehm, Sven-Volker, Lakshmi Goel, and Iris Junglas. "Using Information Systems in Innovation Networks: Uncovering Network Resources." Journal of the Association for Information Systems 18, no. 8 (August 2017): 577–604. http://dx.doi.org/10.17705/1jais.00465.

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30

Polishchuk, O. D. "Centrality in Complex Networks and Betweenness in Network Systems." Mathematical and computer modelling. Series: Physical and mathematical sciences, no. 19 (June 25, 2019): 118–24. http://dx.doi.org/10.32626/2308-5878.2019-19.118-124.

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31

Kloxin, Christopher J., and Christopher N. Bowman. "Covalent adaptable networks: smart, reconfigurable and responsive network systems." Chem. Soc. Rev. 42, no. 17 (April 12, 2013): 7161–73. http://dx.doi.org/10.1039/c3cs60046g.

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32

Dutta, Prabal, and Adam Dunkels. "Operating systems and network protocols for wireless sensor networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 370, no. 1958 (January 13, 2012): 68–84. http://dx.doi.org/10.1098/rsta.2011.0330.

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Анотація:

Sensor network protocols exist to satisfy the communication needs of diverse applications, including data collection, event detection, target tracking and control. Network protocols to enable these services are constrained by the extreme resource scarcity of sensor nodes—including energy, computing, communications and storage—which must be carefully managed and multiplexed by the operating system. These challenges have led to new protocols and operating systems that are efficient in their energy consumption, careful in their computational needs and miserly in their memory footprints, all while discovering neighbours, forming networks, delivering data and correcting failures.

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33

Sohn, Insoo. "Small-World and Scale-Free Network Models for IoT Systems." Mobile Information Systems 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/6752048.

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Анотація:

It is expected that Internet of Things (IoT) revolution will enable new solutions and business for consumers and entrepreneurs by connecting billions of physical world devices with varying capabilities. However, for successful realization of IoT, challenges such as heterogeneous connectivity, ubiquitous coverage, reduced network and device complexity, enhanced power savings, and enhanced resource management have to be solved. All these challenges are heavily impacted by the IoT network topology supported by massive number of connected devices. Small-world networks and scale-free networks are important complex network models with massive number of nodes and have been actively used to study the network topology of brain networks, social networks, and wireless networks. These models, also, have been applied to IoT networks to enhance synchronization, error tolerance, and more. However, due to interdisciplinary nature of the network science, with heavy emphasis on graph theory, it is not easy to study the various tools provided by complex network models. Therefore, in this paper, we attempt to introduce basic concepts of graph theory, including small-world networks and scale-free networks, and provide system models that can be easily implemented to be used as a powerful tool in solving various research problems related to IoT.

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34

Williams, Matthew J., and Mirco Musolesi. "Spatio-temporal networks: reachability, centrality and robustness." Royal Society Open Science 3, no. 6 (June 2016): 160196. http://dx.doi.org/10.1098/rsos.160196.

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Recent advances in spatial and temporal networks have enabled researchers to more-accurately describe many real-world systems such as urban transport networks. In this paper, we study the response of real-world spatio-temporal networks to random error and systematic attack, taking a unified view of their spatial and temporal performance. We propose a model of spatio-temporal paths in time-varying spatially embedded networks which captures the property that, as in many real-world systems, interaction between nodes is non-instantaneous and governed by the space in which they are embedded. Through numerical experiments on three real-world urban transport systems, we study the effect of node failure on a network's topological, temporal and spatial structure. We also demonstrate the broader applicability of this framework to three other classes of network. To identify weaknesses specific to the behaviour of a spatio-temporal system, we introduce centrality measures that evaluate the importance of a node as a structural bridge and its role in supporting spatio-temporally efficient flows through the network. This exposes the complex nature of fragility in a spatio-temporal system, showing that there is a variety of failure modes when a network is subject to systematic attacks.

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35

Rutkowska, Danuta, and Yoichi Hayashi. "Neuro-Fuzzy Systems Approaches." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 3 (June 20, 1999): 177–85. http://dx.doi.org/10.20965/jaciii.1999.p0177.

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Анотація:

Two major approaches to neuro-fuzzy systems are distinguished in the paper. The previous one refers to fuzzy neural networks, which are neural networks with fuzzy signals, and/or fuzzy weights, as well as fuzzy transfer functions. The latter approach concerns neuro-fuzzy systems in the form of multilayer feed-forward networks, which differ from standard neural networks, because elements of particular layers conduct different operations than standard neurons. These structures are neural network representations of fuzzy systems and they are also called connectionist models of fuzzy systems, adaptive fuzzy systems, fuzzy inference neural networks, etc. Two different defuzzifiers, applied to fuzzy systems, are in focus of the paper. Center-of-sums method is an example of parametric defuzzification. Standard neural networks a defuzzifier presents nonparametric approach to defuzzification. For both cases learning algorithms of neuro-fuzzy systems are proposed. These algorithms take a form of recursions derived based on the momentum back-propagation method. Computer simulation demonstrates a comparison between performance of neuro-fuzzy systems with the parametric and nonparametric defuzzifier. Truck backer-upper control problem has been used to illustrate the systems performance. Conclusions concerning the simulation results are summarized. The paper pertains many references on neuro-fuzzy systems, especially selected publications of Czogala, whom it is dedicated.

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36

Efrati, T., and H. Flashner. "Neural Network Based Tracking Control of Mechanical Systems." Journal of Dynamic Systems, Measurement, and Control 121, no. 1 (March 1, 1999): 148–54. http://dx.doi.org/10.1115/1.2802435.

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Анотація:

A method for tracking control of mechanical systems based on artificial neural networks is presented. The controller consists of a proportional plus derivative controller and a two-layer feedforward neural network. It is shown that the tracking error of the closed-loop system goes to zero while the control effort is minimized. Tuning of the neural network’s weights is formulated in terms of a constrained optimization problem. The resulting algorithm has a simple structure and requires a very modest computation effort. In addition, the neural network’s learning procedure is implemented on-line.

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37

Manley, Thomas, Susan Ronning, and William Scheible. "Defining Critical Communications Networks: Modelling Networks as Systems." INSIGHT 23, no. 2 (June 2020): 36–42. http://dx.doi.org/10.1002/inst.12296.

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38

Alaskar, Azzan, and Abdulaziz Alkuhayli. "Reliability Evaluation of Active Distribution Systems with Distributed Generations." IOP Conference Series: Earth and Environmental Science 1026, no. 1 (June 1, 2022): 012064. http://dx.doi.org/10.1088/1755-1315/1026/1/012064.

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Abstract Reliability evaluation is essential in designing, planning, operating modern power systems. System operators must operate the network securely and efficiently with minimal interruption events. With the recent advances in power electronics and control, distributed generations (DG) such as photovoltaic (PV), wind turbine, and storage systems are expected to grow in distribution networks. This high level of distributed generations penetration in the grid can increase the complexity of operating the system. This is caused by intermittent nature of solar irradiance and wind speed. This paper proposes a methodology used to assess distribution networks containing stochastic resources such as photovoltaic. This method will use the Monte Carlo simulation with a stochastic model to evaluate the distribution network’s reliability. The system and load point reliability indices such as frequency of loss of load and expected energy not to supplied will be computed in this technique. In addition, the configuration of distribution networks to improve system’s reliability to facilitate system restoration after pre-fault conditions will be assessed.

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39

KONOVALOV, S. "FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS." Digital Technologies 26 (2019): 36–46. http://dx.doi.org/10.33243/2313-7010-26-36-46.

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In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

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40

Katz, Michael L., and Carl Shapiro. "Systems Competition and Network Effects." Journal of Economic Perspectives 8, no. 2 (May 1, 1994): 93–115. http://dx.doi.org/10.1257/jep.8.2.93.

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This paper discusses firm behavior, market performance, and the public and private institutions that arise in systems markets, i.e., markets where consumers use compatible components together to generate benefits. In such markets, which include communications networks and ‘hardware/software’ networks, popular products are inherently more valuable. These ‘network effects’ can drive corporate strategies and are critical in understanding innovation in many high-technology markets. The discussion here emphasizes the dynamics of consumer adoption decisions in the presence of network effects, competition between incompatible systems, and how suppliers choose which components are compatible and which are not.

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41

Saad, Christian, Bernhard Bauer, Ulrich R. Mansmann, and Jian Li. "AutoAnalyze in Systems Biology." Bioinformatics and Biology Insights 13 (January 2019): 117793221881845. http://dx.doi.org/10.1177/1177932218818458.

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AutoAnalyze is a highly customizable framework for the visualization and analysis of large-scale model graphs. Originally developed for use in the automotive domain, it also supports efficient computation within molecular networks represented by reaction equations. A static analysis approach is used for efficient treatment-condition-specific simulation. The chosen method relies on the computation of a global network data-flow resulting from the evaluation of individual genetic data. The approach facilitates complex analyses of biological components from a molecular network under specific therapeutic perturbations, as demonstrated in a case study. In addition to simulating the complex networks in a stable and reproducible way, kinetic constants can also be fine-tuned using a genetic algorithm and built-in statistical tools.

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42

Boyanov, Petar. "PERFORMANCE AND CONNECTION ANALYSIS OF THE COMPUTER SYSTEMS AND NETWORKS." Journal Scientific and Applied Research 6, no. 1 (December 1, 2014): 141–47. http://dx.doi.org/10.46687/jsar.v6i1.151.

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Анотація:

In this paper a performance and connection analysis of determined computer systems and networks is made. Each communication process and state must be daily scanned and analyzed by system administrators, security-professionals and IT specialists. Ensuring a secure flow of computer and network processes and performance is very important priority for many large and small companies and for normal users. Most of the malicious user and programmers use vulnerability in the selected network via determined computer and network processes.

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43

Moritz, Markus, Daniel Fuchs, and Marian Gheorghe. "Impact of Hidden Networks in Business Systems." Materials Science Forum 957 (June 2019): 247–54. http://dx.doi.org/10.4028/www.scientific.net/msf.957.247.

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Анотація:

In general, networks in companies or between companies play a significant role for monetary as well as non-monetary enhancement through cooperation. The aim is a positive effect for reducing innovation cycles, reducing costs and establishing a well-balanced time to market strategy. Besides open networks, where every actor is known, the existence of hidden networks, internal as well as external, have a substantial impact on strategic and operational activities meaning either a contribution or threat for the actors outside the hidden network. With the new models introduced in this publication, actors in various environments are able to identify hidden networks in order to be able to push contributions or eliminate risks leading from profit cuts to illegal knowledge transfer.

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44

Evans, Joseph, and Zoran Zvonar. "Radio Communications: Components, Systems, and Networks [IEEE Radio Communications Components, Systems, and Networks]." IEEE Communications Magazine 45, no. 12 (December 2007): 38. http://dx.doi.org/10.1109/mcom.2007.4395361.

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45

Aitken, Jonathan M., Rob Alexander, Tim Kelly, and Simon Poulding. "Evolving robust networks for systems-of-systems: is it viable for large networks?" Empirical Software Engineering 19, no. 5 (February 13, 2014): 1502–30. http://dx.doi.org/10.1007/s10664-013-9295-3.

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46

Vaabel, J., T. Koppel, L. Ainola, and L. Sarv. "Capacity reliability of water distribution systems." Journal of Hydroinformatics 16, no. 3 (November 13, 2013): 731–41. http://dx.doi.org/10.2166/hydro.2013.040.

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Hydraulic power capacity of the water distribution network (WDN) is analyzed, and energetically maximum flows in pipes and networks are determined. The concept of hydraulic power for the analysis of WDN characteristics is presented. Hydraulic power capacity characterizes the WDN capacity to meet pressure and flow demands. A capacity reliability indicator called the surplus power factor is introduced for individual transmission pipes and for distribution networks. The surplus power factor s that characterizes the reliability of the hydraulic system can be used along with other measures developed to quantify the hydraulic reliability of water networks. The coefficient of the hydraulic efficiency ηn of the network is defined. A water distribution system in service is analyzed to demonstrate the s and ηn values in the water network in service under different demand conditions. In order to calculate the s factor for WDNs, a network resistance coefficient C was determined. The coefficient C characterizes overall head losses in water pipelines and is a basis for the s factor calculation. This paper presents a theoretical approach to determine the coefficient C through matrix equations.

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47

Multaheb, Samim Ahmad, Bernd Zimmering, and Oliver Niggemann. "Expressing uncertainty in neural networks for production systems." at - Automatisierungstechnik 69, no. 3 (March 1, 2021): 221–30. http://dx.doi.org/10.1515/auto-2020-0122.

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Abstract The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network’s ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.

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48

Zemrane, Hamza, Youssef Baddi, and Abderrahim Hasbi. "Mobile Ad Hoc Network Routing Protocols for Intelligent Transportation Systems." International Journal of Smart Security Technologies 8, no. 1 (January 2021): 35–48. http://dx.doi.org/10.4018/ijsst.2021010103.

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Анотація:

Nowadays, the use of wireless technology has invaded the market of telecommunications networks. There are two main families of wireless networks: networks with infrastructure and networks without infrastructure or AdHoc. In the networks with infrastructure, communications are made via a fixed base station; however, communications in an AdHoc network occur in the absence of any fixed communication infrastructure. There are three types of AdHoc networks: mobile AdHoc networks, mesh networks, and sensor networks. This research applied the mobile AdHoc network to the intelligent transportation systems that improve the traditional transport system with reducing the risk of human errors and with the effective management of traffic congestion. The authors focus in the work especially on the routing protocols inside the MANETs to come out with four routing protocols (AODV, OLSR, DSR, and TORA), and this paper compared their performances in case of using HTTP application, FTP application, email application, voice application, and the rate of the routing control traffic.

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49

Hassouneh, Wissam, Rached Dhaouadi, and Yousef Al-Assaf. "Dynamic Modeling of Nonlinear Systems Using Wavelet Networks." Journal of Robotics and Mechatronics 20, no. 1 (February 20, 2008): 178–87. http://dx.doi.org/10.20965/jrm.2008.p0178.

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Анотація:

The purpose of this paper is to present wavelet networks as a new scheme to model nonlinear systems. The capabilities of wavelet networks in function approximation make them appealing for system modeling. The wavelet networks presented are utilized in the dynamic modeling of a nonlinear servomechanism. A new wavelet network scheme is proposed for the identification of the nonlinearity in the servomechanism. Simulation results show the modeling performance of both schemes.

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50

Keilson, J., and L. D. Servi. "Networks of non-homogeneous M/G/∞ systems." Journal of Applied Probability 31, A (1994): 157–68. http://dx.doi.org/10.2307/3214954.

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Анотація:

For a network of G/∞ service facilities, the transient joint distribution of the facility populations is shown by new simple methods to have a simple Poisson product form with simple explicit formulas for the means. In the network it is assumed that: (a) each facility has an infinite number of servers; (b) the service time distributions are general; (c) external traffic is non-homogeneous in time; (d) arrivals have random or deterministic routes through the network possibly returning to the same facility more than once; (e) arrivals use the facilities on their route sequentially or in parallel (as in the case of a circuitswitched telecommunication network). The results have relevance to communication networks and manufacturing systems.

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