Готові списки джерел за темами / Evolution on networks

Добірка наукової літератури з теми "Evolution on networks"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Evolution on networks"

1

Huang, Ailing, Jie Xiong, Jinsheng Shen, and Wei Guan. "Evolution of weighted complex bus transit networks with flow." International Journal of Modern Physics C 27, no. 06 (May 13, 2016): 1650064. http://dx.doi.org/10.1142/s0129183116500649.

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Study on the intrinsic properties and evolutional mechanism of urban public transit networks (PTNs) has great significance for transit planning and control, particularly considering passengers’ dynamic behaviors. This paper presents an empirical analysis for exploring the complex properties of Beijing’s weighted bus transit network (BTN) based on passenger flow in L-space, and proposes a bi-level evolution model to simulate the development of transit routes from the view of complex network. The model is an iterative process that is driven by passengers’ travel demands and dual-controlled interest mechanism, which is composed of passengers’ spatio-temporal requirements and cost constraint of transit agencies. Also, the flow’s dynamic behaviors, including the evolutions of travel demand, sectional flow attracted by a new link and flow perturbation triggered in nearby routes, are taken into consideration in the evolutional process. We present the numerical experiment to validate the model, where the main parameters are estimated by using distribution functions that are deduced from real-world data. The results obtained have proven that our model can generate a BTN with complex properties, such as the scale-free behavior or small-world phenomenon, which shows an agreement with our empirical results. Our study’s results can be exploited to optimize the real BTN’s structure and improve the network’s robustness.
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2

Liu, Yaqin, Yunsi Chen, Qing He, and Qian Yu. "Cyclical Evolution of Emerging Technology Innovation Network from a Temporal Network Perspective." Systems 11, no. 2 (February 5, 2023): 82. http://dx.doi.org/10.3390/systems11020082.

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With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution.
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3

Manrubia, Susanna C., and José A. Cuesta. "Neutral networks of genotypes: evolution behind the curtain." Arbor 186, no. 746 (December 30, 2010): 1051–64. http://dx.doi.org/10.3989/arbor.2010.746n1253.

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4

Walker, David M., Antoinette Tordesillas, Amy L. Rechenmacher, and Michael Small. "Multiscale resolution of networks of granular media network evolution—a network of networks." IEICE Proceeding Series 2 (March 17, 2014): 294–97. http://dx.doi.org/10.15248/proc.2.294.

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5

Tupikina, L., K. Rehfeld, N. Molkenthin, V. Stolbova, N. Marwan, and J. Kurths. "Characterizing the evolution of climate networks." Nonlinear Processes in Geophysics 21, no. 3 (June 25, 2014): 705–11. http://dx.doi.org/10.5194/npg-21-705-2014.

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Abstract. Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, Erdős–Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970–2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.
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6

Besjedica, Toni, Krešimir Fertalj, Vlatko Lipovac, and Ivona Zakarija. "Evolution of Hybrid LiFi–WiFi Networks: A Survey." Sensors 23, no. 9 (April 25, 2023): 4252. http://dx.doi.org/10.3390/s23094252.

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Given the growing number of devices and their need for internet access, researchers are focusing on integrating various network technologies. Concerning indoor wireless services, a promising approach in this regard is to combine light fidelity (LiFi) and wireless fidelity (WiFi) technologies into a hybrid LiFi and WiFi network (HLWNet). Such a network benefits from LiFi’s distinct capability for high-speed data transmission and from the wide radio coverage offered by WiFi technologies. In this paper, we describe the framework for the HWLNet architecture, providing an overview of the handover methods used in HLWNets and presenting the basic architecture of hybrid LiFi/WiFi networks, optimization of cell deployment, relevant modulation schemes, illumination constraints, and backhaul device design. The survey also reviews the performance and recent achievements of HLWNets compared to legacy networks with an emphasis on signal to noise and interference ratio (SINR), spectral and power efficiency, and quality of service (QoS). In addition, user behaviour is discussed, considering interference in a LiFi channel is due to user movement, handover frequency, and load balancing. Furthermore, recent advances in indoor positioning and the security of hybrid networks are presented, and finally, directions of the hybrid network’s evolution in the foreseeable future are discussed.
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7

Li, Zhang-Wei, Xu-Hua Yang, Feng-Ling Jiang, Guang Chen, Guo-Qing Weng, and Mei Zhu. "Dynamically Weighted Clique Evolution Model in Clique Networks." Mathematical Problems in Engineering 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/182638.

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This paper proposes a weighted clique evolution model based on clique (maximal complete subgraph) growth and edge-weight driven for complex networks. The model simulates the scheme of real-world networks that the evolution of networks is likely to be driven by the flow, such as traffic or information flow needs, as well as considers that real-world networks commonly consist of communities. At each time step of a network’s evolution progress, an edge is randomly selected according to a preferential scheme. Then a new clique which contains the edge is added into the network while the weight of the edge is adjusted to simulate the flow change brought by the new clique addition. We give the theoretical analysis based on the mean field theory, as well as some numerical simulation for this model. The result shows that the model can generate networks with scale-free distributions, such as edge weight distribution and node strength distribution, which can be found in many real-world networks. It indicates that the evolution rule of the model may attribute to the formation of real-world networks.
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8

Pachón, Alvaro. "Networks Architecture Evolution." Sistemas y Telemática 1, no. 1 (July 28, 2006): 77. http://dx.doi.org/10.18046/syt.v1i1.1079.

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9

Dorogovtsev, S. N., and J. F. F. Mendes. "Evolution of networks." Advances in Physics 51, no. 4 (June 2002): 1079–187. http://dx.doi.org/10.1080/00018730110112519.

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Tian, Yang, Guoqi Li, and Pei Sun. "Information evolution in complex networks." Chaos: An Interdisciplinary Journal of Nonlinear Science 32, no. 7 (July 2022): 073105. http://dx.doi.org/10.1063/5.0096009.

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Many biological phenomena or social events critically depend on how information evolves in complex networks. However, a general theory to characterize information evolution is yet absent. Consequently, numerous unknowns remain about the mechanisms underlying information evolution. Among these unknowns, a fundamental problem, being a seeming paradox, lies in the coexistence of local randomness, manifested as the stochastic distortion of information content during individual–individual diffusion, and global regularity, illustrated by specific non-random patterns of information content on the network scale. Here, we attempt to formalize information evolution and explain the coexistence of randomness and regularity in complex networks. Applying network dynamics and information theory, we discover that a certain amount of information, determined by the selectivity of networks to the input information, frequently survives from random distortion. Other information will inevitably experience distortion or dissipation, whose speeds are shaped by the diversity of information selectivity in networks. The discovered laws exist irrespective of noise, but noise accounts for disturbing them. We further demonstrate the ubiquity of our discovered laws by analyzing the emergence of neural tuning properties in the primary visual and medial temporal cortices of animal brains and the emergence of extreme opinions in social networks.
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Дисертації з теми "Evolution on networks"

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Willmann, Stana. "Evolution of genetic networks." [S.l. : s.n.], 2005. http://deposit.ddb.de/cgi-bin/dokserv?idn=973677864.

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Rizzi, Giacomo. "Genetic Evolution of Neural Networks." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16769/.

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Evolutionary computation has been around ever since the late 50s. This thesis aims at elaborate on genetic algorithms, a subset of evolutionary computation, with particular regard to the field of neuroevolution, which is the application of GAs to the generation of functioning neural networks. The most widely adopted techniques are thereby explained and contrasted. The experimentation chapter finally shows an implementation of a genetic algorithm, inspired by existing algorithms, with the objective of optimizing a novel kind of artificial neural network.
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3

Trusina, Ala. "Complex Networks : Structure, Function , Evolution." Doctoral thesis, Umeå University, Physics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-608.

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A complex system is a system for which the statement "the whole is greater than the sum of its parts" holds. A network can be viewed as a backbone of a complex system. Combining the knowledge about the entities constituting the complex system with the properties of the interaction patterns we can get a better understanding of why the whole is greater than the sum. One of the purposes of network studies, is to relate the particular structural and dynamical properties of the network to the function it is designed to perform. In the present work I am briefly presenting some of the advances that have been achieved in the field of the complex networks together with the contributions which I have been involved in.

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Landassuri, Moreno Victor Manuel. "Evolution of modular neural networks." Thesis, University of Birmingham, 2012. http://etheses.bham.ac.uk//id/eprint/3243/.

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It is well known that the human brain is highly modular, having a structural and functional organization that allows the different regions of the brain to be reused for different cognitive processes. So far, this has not been fully addressed by artificial systems, and a better understanding of when and how modules emerge is required, with a broad framework indicating how modules could be reused within neural networks. This thesis provides a deep investigation of module formation, module communication (interaction) and module reuse during evolution for a variety of classification and prediction tasks. The evolutionary algorithm EPNet is used to deliver the evolution of artificial neural networks. In the first stage of this study, the EPNet algorithm is carefully studied to understand its basis and to ensure confidence in its behaviour. Thereafter, its input feature selection (required for module evolution) is optimized, showing the robustness of the improved algorithm compared with the fixed input case and previous publications. Then module emergence, communication and reuse are investigated with the modular EPNet (M-EPNet) algorithm, which uses the information provided by a modularity measure to implement new mutation operators that favour the evolution of modules, allowing a new perspective for analyzing modularity, module formation and module reuse during evolution. The results obtained extend those of previous work, indicating that pure-modular architectures may emerge at low connectivity values, where similar tasks may share (reuse) common neural elements creating compact representations, and that the more different two tasks are, the bigger the modularity obtained during evolution. Other results indicate that some neural structures may be reused when similar tasks are evolved, leading to module interaction during evolution.
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Mohan, Madan Babu. "Evolution of transcriptional regulatory networks." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.616113.

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Whitaker, John William. "On the evolution of metabolic networks." Thesis, University of Leeds, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.511155.

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Opsahl, Tore. "Structure and Evolution of Weighted Networks." Thesis, Queen Mary, University of London, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.507253.

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Cooper, Max B. "Evolution of small gene regulatory networks." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495599.

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Alotaibi, Sultan. "3GPP Long Term Evolution LTE Scheduling." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc490046/.

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Future generation cellular networks are expected to deliver an omnipresent broadband access network for an endlessly increasing number of subscribers. Long term Evolution (LTE) represents a significant milestone towards wireless networks known as 4G cellular networks. A key feature of LTE is the implementation of enhanced Radio Resource Management (RRM) mechanism to improve the system performance. The structure of LTE networks was simplified by diminishing the number of the nodes of the core network. Also, the design of the radio protocol architecture is quite unique. In order to achieve high data rate in LTE, 3rd Generation Partnership Project (3GPP) has selected Orthogonal Frequency Division Multiplexing (OFDM) as an appropriate scheme in terms of downlinks. However, the proper scheme for an uplink is the Single-Carrier Frequency Domain Multiple Access due to the peak-to-average-power-ratio (PAPR) constraint. LTE packet scheduling plays a primary role as part of RRM to improve the system’s data rate as well as supporting various QoS requirements of mobile services. The major function of the LTE packet scheduler is to assign Physical Resource Blocks (PRBs) to mobile User Equipment (UE). In our work, we formed a proposed packet scheduler algorithm. The proposed scheduler algorithm acts based on the number of UEs attached to the eNodeB. To evaluate the proposed scheduler algorithm, we assumed two different scenarios based on a number of UEs. When the number of UE is lower than the number of PRBs, the UEs with highest Channel Quality Indicator (CQI) will be assigned PRBs. Otherwise, the scheduler will assign PRBs based on a given proportional fairness metric. The eNodeB’s throughput is increased when the proposed algorithm was implemented.
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Catanese, Salvatore Amato. "New perspectives in criminal network analysis: multilayer networks, time evolution, and visualization." Doctoral thesis, Università di Catania, 2017. http://hdl.handle.net/10761/3793.

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The work presented in this Dissertation reflects a long-term human, professional and cultural path started some years ago when I first developed LogAnalysis, a tool for the analysis and visualization of criminal and social networks. Since then, I devoted myself to the development of frameworks, algorithms and techniques for supporting intelligence and law enforcement agencies in the task of unveiling the CN structure hidden in communication data, identifying the target offenders for their removal or selecting effective strategies to disrupt a criminal organization. In a natural way, I successively focused on the evaluation of the resilience of criminal networks and on the multiplex formalism, which takes into account the various relationships existing within a criminal organization. In this context I introduce criminal network analysis tools: LogAnalysis, LogViewer, Semantic viewer and Failure simulator. I have been involved in the design, modeling, and writing of all of the works presented. In particular, I have also developed and tested all the visual tools included therein. Finally, I introduce Multiplex PBFS (Mx-PBFS) a novel multi-threaded parallel Breadth-First Search algorithm for categorical and inter-layer couplings multiplex networks, and the framework CriMuxnet (still under development) for multilayer criminal networks analysis based on high-quality 3D visualizations of network data. CriMuxnet was designed to work in conjunction with a 3D computer graphics (CG) packages: Autodesk Maya or Blender. CriMuxnet exploits 3D engine features to significantly improve both exploratory search and visualization strategy.
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Книги з теми "Evolution on networks"

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Punz, Gottfried. Evolution of 3G Networks. Vienna: Springer Vienna, 2010. http://dx.doi.org/10.1007/978-3-211-09440-2.

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Carneiro, Mauricio Oliveira. Evolution of biological networks. Cambridge, Mass: Harvard University, 2010.

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3

Buchmann, Tobias. The Evolution of Innovation Networks. Wiesbaden: Springer Fachmedien Wiesbaden, 2015. http://dx.doi.org/10.1007/978-3-658-10383-5.

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4

Neil, Turok, Fermi National Accelerator Laboratory, and United States. National Aeronautics and Space Administration., eds. Evolution of cosmic string networks. Batavia, IL: Fermi National Accelerator Laboratory, 1989.

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5

1949-, Pujolle G., ed. Management, control, and evolution of IP networks. London, UK: ISTE, 2006.

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6

Chowdary, P. Satish Rama, Jaume Anguera, Suresh Chandra Satapathy, and Vikrant Bhateja, eds. Evolution in Signal Processing and Telecommunication Networks. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8554-5.

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7

Welch, Michael John, Mikael Lüthje, and Simon John Oldfield. Modelling the Evolution of Natural Fracture Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52414-2.

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Mugnolo, Delio. Semigroup Methods for Evolution Equations on Networks. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04621-1.

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Pujolle, Guy, ed. Management, Control and Evolution of IP Networks. London, UK: ISTE, 2007. http://dx.doi.org/10.1002/9780470612118.

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10

Construction collaboration technologies: The extranet evolution. London: Taylor & Francis, 2005.

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Частини книг з теми "Evolution on networks"

1

Hardy, Daniel, Guy Malléus, and Jean-Noël Méreur. "The evolution of telecommunications regulation." In Networks, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55498-8_1.

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2

Wagner, Andreas. "Metabolic Networks, Evolution." In Encyclopedia of Systems Biology, 1256–59. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_4.

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Vaezi, Mojtaba, and Ying Zhang. "Radio Access Network Evolution." In Wireless Networks, 67–86. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54496-0_6.

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Prasad, Ramjee, and Fernando J. Velez. "The Evolution Towards WiMAX." In WiMAX Networks, 1–62. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8752-2_1.

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Hardy, Daniel, Guy Malléus, and Jean-Noël Méreur. "The evolution of the telecommunications market." In Networks, 13–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55498-8_2.

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Jha, Vikas Kumar, Bishwajeet Pandey, and Ciro Rodriguez Rodriguez. "Wireless Networks." In Network Evolution and Applications, 99–119. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003302902-6.

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Martinez-Morales, Juan R., and Annamaria Locascio. "Vertebrate Eye Evolution." In Organogenetic Gene Networks, 275–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42767-6_10.

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Rast, Jonathan P. "Development gene networks and evolution." In Genome Evolution, 225–34. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0263-9_22.

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Holma, Harri, and Fernando Sanchez Moya. "Heterogeneous Networks." In HSPA+ Evolution to Release 12, 117–37. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118693728.ch7.

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Conti, Marco, Enrico Gregori, and Luciano Lenzini. "Evolution Towards Gigabit Rates." In Metropolitan Area Networks, 393–407. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0909-9_9.

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Тези доповідей конференцій з теми "Evolution on networks"

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Santana, Clodomir, Edward Keedwell, and Ronaldo Menezes. "Networks of evolution." In GECCO '22: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3520304.3529039.

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Ciarniello, Alberto. "Networks 2008 - Wireless Evolution Panel Session." In 2008 13th International Telecommunications Network Strategy and Planning Symposium (NETWORKS). IEEE, 2008. http://dx.doi.org/10.1109/netwks.2008.4763749.

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Ciarniello, Alberto. "Networks 2008 - wireless evolution panel session." In 2008 13th International Telecommunications Network Strategy and Planning Symposium (NETWORKS). IEEE, 2008. http://dx.doi.org/10.1109/netwks.2008.6231379.

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Gladisch, Andreas. "Evolution of Optical Networks." In Optical Amplifiers and Their Applications. Washington, D.C.: OSA, 2005. http://dx.doi.org/10.1364/oaa.2005.ma1.

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Bestak, Robert. "Evolution of mobile networks." In 2008 International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE, 2008. http://dx.doi.org/10.1109/iwssip.2008.4604359.

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Miikkulainen, Risto. "Evolution of neural networks." In GECCO '19: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3319619.3323380.

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Miikkulainen, Risto. "Evolution of neural networks." In GECCO '20: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377929.3389858.

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Miikkulainen, Risto. "Evolution of neural networks." In GECCO '17: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3067695.3067716.

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Miikkulainen, Risto. "Evolution of neural networks." In GECCO '18: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3205651.3207857.

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Miikkulainen, Risto. "Evolution of neural networks." In GECCO '21: Genetic and Evolutionary Computation Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3449726.3461432.

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Звіти організацій з теми "Evolution on networks"

1

Albrecht, A., and N. Turok. Evolution of cosmic string networks. Office of Scientific and Technical Information (OSTI), June 1989. http://dx.doi.org/10.2172/5823780.

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2

Carvalho, Vasco, and Nico Voigtländer. Input Diffusion and the Evolution of Production Networks. Cambridge, MA: National Bureau of Economic Research, March 2014. http://dx.doi.org/10.3386/w20025.

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3

Sarikaya, B., F. Xia, and T. Lemon. DHCPv6 Prefix Delegation in Long-Term Evolution (LTE) Networks. RFC Editor, July 2012. http://dx.doi.org/10.17487/rfc6653.

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4

Thompson, Geoffrey, and Kathryn M. Gillis. Evolution of Fracture Networks in the Upper Oceanic Crust. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada326937.

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5

Mamede, Ricardo. Labour Mobility, Industry Evolution and Social Networks: A Co-Evolutionary Model. DINÂMIA'CET-IUL, 2008. http://dx.doi.org/10.7749/dinamiacet-iul.wp.2008.67.

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6

Templeton, D. C. Evolution of Induced Fracture Networks Using Machine Learning Correlation Image Analysis. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1572626.

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Reeves, Paul C., and Sean Andrew McKenna. Evolution of neural networks for the prediction of hydraulic conductivity as a function of borehole geophysical logs: Shobasama site, Japan. Office of Scientific and Technical Information (OSTI), June 2004. http://dx.doi.org/10.2172/918374.

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Soare, Sorina. Romanian populism and transnational political mobilization. European Center for Populism Studies (ECPS), March 2023. http://dx.doi.org/10.55271/rp0027.

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Once considered a partial exception to the recent diffusion of populism worldwide, Romania saw Radical Right populism return to Parliament in 2020. The Alliance for the Union of Romanians (AUR) successfully campaigned on a platform of defending the Christian faith, freedom, the traditional family, and the nation. Although the party was initially considered the result of individual entrepreneurship linked to its founding leaders, it has successfully built on diffused networks of societal activism whose origins could be traced back to the early 2000s. However, the AUR’s track record of discourse aligned with Kremlin rhetoric calling for Western economic, political and cultural hegemony to be resisted and rolled back saw a temporary decline in voters’ support for the party. However, the party managed to rebuild consensus strategically by drawing on voters’ increased anxiety regarding the economic effects of the war. This report offers a cogent analysis of the political performance of the AUR, examining the party’s formative phase as well as its evolution since 2020, alongside a discussion of the impact of the war in Ukraine on Romanian party politics.
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Biswas, Sukalpa, and Alex Wright. The Digital Evolution of Highway Infrastructure Asset Management. TRL, July 2022. http://dx.doi.org/10.58446/cwlm6036.

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The last decade has seen a very gradual change at a local network level in the methods used to collect data from Highways Infrastructure Assets, how to manage and operate Assets, a change in the way those Assets are used, and the way they are maintained and constructed. This change is largely driven by digital technology improvements. This evolution has also created uncertainty around Highways Asset Management processes. The sector is now at a point where there is an imminent requirement for transition from “Traditional” to “Intelligent” styles of Asset Management. While the strategic network operators have been invested in this transformation for some time, Local Road operators are being left behind. In this paper TRL identifies some of the key actions required for local highways managers to successfully achieve this transition to digital, and in so doing realise the benefits of the digital evolution of highways Asset Management. Dr Sukalpa Biswas and Dr Alex Wright touch on the factors influencing the changes, identify the novel technologies having the greatest impact, and set out five essential steps for Asset owners to consider on their journey to Intelligent Asset Management. This is accompanied by specific practical actions required of each type of stakeholder (Asset Owners, Technology & Software Providers, Standards & Legislative Authorities).
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Cepeda-López, Freddy Hernán, Fredy Alejandro Gamboa-Estrada, Carlos Eduardo León-Rincón, and Hernán Rincón-Castro. The evolution of world trade from 1995 to 2014 : a network approach. Bogotá, Colombia: Banco de la República, February 2017. http://dx.doi.org/10.32468/be.985.

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