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Artykuły w czasopismach na temat „Network modelling”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 25 lipca 2025

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1

POLNOV, O., K. HORIASHCHENKO, and V. MISHAN. "MODERN NETWORK MODELLING SOFTWARE." Herald of Khmelnytskyi National University. Technical sciences 289, no. 5 (2020): 35–38. https://doi.org/10.31891/2307-5732-2020-289-5-35-38.

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Modern telecommunication network consist of large amount of components like servers, routers, switches, end-point computers. Fibre channels and usual gigabit lines used as a medium. Modern network can combine not only static elements but mobile components like mobile phones, ZigBee modules, Wi-Fi access points, Bluetooth. High speed equipment like satellites can be added to network structure too. So, selecting of correct equipment for network, estimating characteristics of such network can be hard task to be done in most cases. The analysis of modern programmatic facilities of design of work o

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Smirnov, Alexander, and Nikolay Shilov. "Business Network Modelling." International Journal of Information System Modeling and Design 1, no. 4 (2010): 77–91. http://dx.doi.org/10.4018/jismd.2010100104.

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Business networks have appeared as a reaction to changes taking place in the world economy and logistic networks can be considered as examples of such networks. The approach proposed in the paper is based on the idea to represent the business network members with services provided by them, and to achieve interoperability via application of the SOA standards. The approach is based on usage of such technologies as Web services, ontology, and context management. Web services enable interoperability at the technological level. Ontologies are used for description of knowledge domains and enable int

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Maciá-Pérez, Francisco, Iren Lorenzo-Fonseca, Jose Vicente Berná-Martinez, and Jose Manuel Sánchez-Bernabeu. "Conceptual Modelling of Complex Network Management Systems." Journal of Computers 10, no. 5 (2015): 309–20. http://dx.doi.org/10.17706/jcp.10.5.309-320.

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Pugacheva, E. "Social Network Modelling." Мiждисциплiнарнi дослiдження складних систем, no. 6 (2015): 49–62. http://dx.doi.org/10.31392/2307-4515/2015-6.2.

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Clark, J. W. "Neural network modelling." Physics in Medicine and Biology 36, no. 10 (1991): 1259–317. http://dx.doi.org/10.1088/0031-9155/36/10/001.

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Lambert, Jean, and Béatrice Stéphan. "Modelling network profiles." Computer Communications 18, no. 9 (1995): 600–608. http://dx.doi.org/10.1016/0140-3664(95)99803-k.

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Anderson, Taylor, and Suzana Dragićević. "Representing Complex Evolving Spatial Networks: Geographic Network Automata." ISPRS International Journal of Geo-Information 9, no. 4 (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

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Cebolledo Gutierrez, Edgar Omar, and Olga De Troyer. "Modelling Social Network Interactions in Games." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 11, no. 4 (2021): 82–88. http://dx.doi.org/10.1609/aiide.v11i4.12831.

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Social Networks, like Facebook, have become an important part of life for millions of people, with many people using them on a daily basis. While there are games that simulate life-to-life interactions, there are not as many that simulate social network interactions, and even fewer where social network interactions are an important part of the gameplay. Having models for the simulation of interactions on social networks is the first step needed to be able to create games where a social network is an important component of the gameplay. In this paper we present a model to create dy-namic and be

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Tumbas, Pere, Predrag Matkovic, and Marton Sakal. "Modelling cross-border regional network for innovative development." Perspectives of Innovations, Economics and Business 13, no. 3 (2013): 16–24. http://dx.doi.org/10.15208/pieb.2013.13.

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Prinz, Astrid A. "Computational approaches to neuronal network analysis." Philosophical Transactions of the Royal Society B: Biological Sciences 365, no. 1551 (2010): 2397–405. http://dx.doi.org/10.1098/rstb.2010.0029.

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Computational modelling is an approach to neuronal network analysis that can complement experimental approaches. Construction of useful neuron and network models is often complicated by a variety of factors and unknowns, most notably the considerable variability of cellular and synaptic properties and electrical activity characteristics found even in relatively ‘simple’ networks of identifiable neurons. This chapter discusses the consequences of biological variability for network modelling and analysis, describes a way to embrace variability through ensemble modelling and summarizes recent fin

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Fischer, M. M. "Computational Neural Networks: A New Paradigm for Spatial Analysis." Environment and Planning A: Economy and Space 30, no. 10 (1998): 1873–91. http://dx.doi.org/10.1068/a301873.

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In this paper a systematic introduction to computational neural network models is given in order to help spatial analysts learn about this exciting new field. The power of computational neural networks viz-à-viz conventional modelling is illustrated for an application field with noisy data of limited record length: spatial interaction modelling of telecommunication data in Austria. The computational appeal of neural networks for solving some fundamental spatial analysis problems is summarized and a definition of computational neural network models in mathematical terms is given. Three definiti

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Ma, Hongyang, Qile Zhao, Sandra Verhagen, Dimitrios Psychas, and Han Dun. "Kriging Interpolation in Modelling Tropospheric Wet Delay." Atmosphere 11, no. 10 (2020): 1125. http://dx.doi.org/10.3390/atmos11101125.

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This contribution implements the Kriging interpolation in predicting the tropospheric wet delays using global navigation satellite system networks. The predicted tropospheric delays can be used in strengthening the precise point positioning models and numerical weather prediction models. In order to evaluate the performances of the Kriging interpolation, a sparse network with 8 stations and a dense network with 19 stations from continuously operating reference stations (CORS) of the Netherlands are selected as the reference. In addition, other 15 CORS stations are selected as users, which are

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Ghaboussi, J., X. Wu, and G. Kaklauskas. "NEURAL NETWORK MATERIAL MODELLING." Statyba 5, no. 4 (1999): 250–57. http://dx.doi.org/10.1080/13921525.1999.10531472.

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Novokhrestov, A., A. Konev, A. Shelupanov, and A. Buymov. "Computer network threat modelling." Journal of Physics: Conference Series 1488 (March 2020): 012002. http://dx.doi.org/10.1088/1742-6596/1488/1/012002.

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Burnell, David, and Julia Race. "Network Asset Performance Modelling." OR Insight 15, no. 1 (2002): 3–14. http://dx.doi.org/10.1057/ori.2002.2.

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Hou, Jennifer C., and P. R. Kumar. "Network modelling and simulation." Computer Networks 50, no. 12 (2006): 1885–86. http://dx.doi.org/10.1016/j.comnet.2005.10.025.

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Cudmore, Peter, and Edmund J. Crampin. "Bondgraphtools: Modelling Network Bioenergetics." Biophysical Journal 116, no. 3 (2019): 420a. http://dx.doi.org/10.1016/j.bpj.2018.11.2262.

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Harby, Atle. "European aquatic modelling network." River Research and Applications 23, no. 5 (2007): 467–68. http://dx.doi.org/10.1002/rra.996.

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O., Amusan, Thompson A. F., Aderinola T. B., and Alese B. K. "Modelling Malicious Attack in Social Networks." Network and Communication Technologies 5, no. 1 (2020): 37. http://dx.doi.org/10.5539/nct.v5n1p37.

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Online Social Networks (OSNs) are based on actual trust relationships in environments which help people communicate with friends, family and acquaintances. Malicious individuals take advantage of this trust relationship to propagate malware through social networks. We study the dynamics of malware propagation among OSN users. Social networks users are referred to as nodes which is in two compartments: Healthy (H), or Infected (I). A H node could either be susceptible to infection (S) or removed (R). Simulations were carried out in R using the EpiModel network simulation package. Two networks w

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20

Anzellotti, Stefano, Dorit Kliemann, Nir Jacoby, and Rebecca Saxe. "Directed network discovery with dynamic network modelling." Neuropsychologia 99 (May 2017): 1–11. http://dx.doi.org/10.1016/j.neuropsychologia.2017.02.006.

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21

Durugbo, Christopher. "Bunch: a channel-oriented business network modelling approach." Business Process Management Journal 20, no. 3 (2014): 455–79. http://dx.doi.org/10.1108/bpmj-02-2013-0017.

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Purpose – The purpose of this paper is to evaluate the benefits of using the business network channel (Bunch) approach for modelling business networks and studying the business network evolution. Business network models put the structures of process models into context by visualising roles and communication channels for social interactions. Design/methodology/approach – The research applies a case study-based approach involving the creation of business network visualisations to capture snapshots of an industrial firm's business network over a three-year period. A questionnaire-based study was

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22

Haq, Hafiz, Petri Välisuo, and Seppo Niemi. "Modelling Sustainable Industrial Symbiosis." Energies 14, no. 4 (2021): 1172. http://dx.doi.org/10.3390/en14041172.

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Industrial symbiosis networks conventionally provide economic and environmental benefits to participating industries. However, most studies have failed to quantify waste management solutions and identify network connections in addition to methodological variation of assessments. This study provides a comprehensive model to conduct sustainable study of industrial symbiosis, which includes identification of network connections, life cycle assessment of materials, economic assessment, and environmental performance using standard guidelines from the literature. Additionally, a case study of indust

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23

Kaveh, M., and R. A. Chayjan. "Mathematical and neural network modelling of terebinth fruit under fluidized bed drying." Research in Agricultural Engineering 61, No. 2 (2016): 55–65. http://dx.doi.org/10.17221/56/2013-rae.

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The paper presents an application which uses Feed Forward Neural Networks (FFNNs) to model the non-linear behaviour of the terebinth fruit drying. Mathematical models and Artificial Neural Networks (ANNs) were used for prediction of effective moisture diffusivity, specific energy consumption, shrinkage, drying rate and moisture ratio in terebinth fruit. Feed Forward Neural Network (FFBP) and Cascade Forward Neural Network (CFNN) as well as training algorithms of Levenberg-Marquardt (LM) and Bayesian regularization (BR) were used. Air temperature and velocity limits were 40&ndash;80&deg

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Vasiliauskas, Aidas Vasilis. "MODELLING OF INTERMODAL FREIGHT TRANSPORTATION NETWORK." TRANSPORT 17, no. 3 (2002): 117–21. http://dx.doi.org/10.3846/16483840.2002.10414024.

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It was realized long ago that one of the main factors for steady groowing economic of the country is a properly working transport system in which new enough means of cargo transportation, so called intermodal transportation takes not the last place. First of all it raises problems related with the development and optimization of proper infrastructure - the network of roads and terminals which aim to carry out intermodal transportation. The article deals with one of possible ways of modelling the national intermodal freight transportation network. The basis for such model is the investigation o

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Kanaev, A., E. Kazakevich, M. Saharova, and F. Proshin. "Modelling of Timestamp Processing within Transport Network Equipment." Proceedings of Telecommunication Universities 10, no. 2 (2024): 34–47. http://dx.doi.org/10.31854/1813-324x-2024-10-2-34-47.

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The modern telecommunication networks are forming on the basis of different transmission and data processing technologies. High load on the transport layer nodes demands quality synchronization of equipment that works on it. The transport network is an important element which delivers the time scale to slave nodes. Using the time-division multiplexing for the high-speed channel processing with the use of wavelength division multiplexing (OTN, Optical Transport Network) and classic Ethernet networks as a transport layer systems needs the precision time synchronization which can be achieved with

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26

Gleiser, Pablo M., and Victor I. Spoormaker. "Modelling hierarchical structure in functional brain networks." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1933 (2010): 5633–44. http://dx.doi.org/10.1098/rsta.2010.0279.

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In this work, we focus on a complex-network approach for the study of the brain. In particular, we consider functional brain networks, where the vertices represent different anatomical regions and the links their functional connectivity. First, we build these networks using data obtained with functional magnetic resonance imaging. Then, we analyse the main characteristics of these complex networks, including degree distribution, the presence of modules and hierarchical structure. Finally, we present a network model with dynamical nodes and adaptive links. We show that the model allows for the

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Massimo, Di Gangf. "Modelling dynamic network loading ontransportation networks through a continuous." Mathematical Modelling of Systems 2, no. 3 (1996): 175–96. http://dx.doi.org/10.1080/13873959608837037.

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Sadyrbaev, Felix, Inna Samuilik, and Valentin Sengileyev. "On Modelling of Genetic Regulatory Net Works." WSEAS TRANSACTIONS ON ELECTRONICS 12 (August 2, 2021): 73–80. http://dx.doi.org/10.37394/232017.2021.12.10.

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We consider mathematical model of genetic regulatory networks (GRN). This model consists of a nonlinear system of ordinary differential equations. The vector of solutions X(t) is interpreted as a current state of a network for a given value of time t: Evolution of a network and future states depend heavily on attractors of system of ODE. We discuss this issue for low dimensional networks and show how the results can be applied for the study of large size networks. Examples and visualizations are provided

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Dave, Kushal, Rushi Bhatt, and Vasudeva Varma. "Modelling Action Cascades in Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (2021): 121–28. http://dx.doi.org/10.1609/icwsm.v5i1.14092.

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The central idea in designing various marketing strategies for online social networks is to identify the influencers in the network. The influential individuals induce ``word-of-mouth" effects in the network. These individuals are responsible for triggering long cascades of influence that convince their peers to perform a similar action (buying a product, for instance). Targeting these influentials usually leads to a vast spread of the information across the network. Hence it is important to identify such individuals in a network. One way to measure an individual's influencing capability on it

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Johnson, Samuel. "Epidemic modelling requires knowledge of the social network." Journal of Physics: Complexity 5, no. 1 (2024): 01LT01. http://dx.doi.org/10.1088/2632-072x/ad19e0.

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Abstract ‘Compartmental models’ of epidemics are widely used to forecast the effects of communicable diseases such as COVID-19 and to guide policy. Although it has long been known that such processes take place on social networks, the assumption of ‘random mixing’ is usually made, which ignores network structure. However, ‘super-spreading events’ have been found to be power-law distributed, suggesting that the underlying networks may be scale free or at least highly heterogeneous. The random-mixing assumption would then produce an overestimation of the herd-immunity threshold for given R 0; an

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Ji, Q., S. Barr, P. James, and D. Fairbairn. "A GEOSPATIAL ANALYSIS FRAMEWORK FOR FINE SCALE URBAN INFRASTRUCTURE NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4 (September 19, 2018): 291–96. http://dx.doi.org/10.5194/isprs-archives-xlii-4-291-2018.

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<p><strong>Abstract.</strong> Understanding the spatial connectivity of urban infrastructure networks that connect assets to buildings is important for the fine-scale spatial analysis and modelling of the resource flows within cities. However, rarely are spatially explicit representations of infrastructure networks available for such analysis. Further, an appropriate database system is the core of development of an infrastructure assets information and management platform, capable of handling the wide range of data for infrastructure system modelling and analysis. In this pap

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Mailhot, Alain, Sophie Duchesne, Emmanuelle Musso, and Jean-Pierre Villeneuve. "Modélisation de l'évolution de l'état structural des réseaux d'égout : application à une municipalité du Québec." Canadian Journal of Civil Engineering 27, no. 1 (2000): 65–72. http://dx.doi.org/10.1139/l99-054.

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Many sanitary and storm sewer networks are old and deteriorating rapidly. Water and sanitation managers are becoming more and more aware of the negative impact of poorly maintained sewer networks, especially since they are asked to comply with stricter environmental standards under already challenging budget constraints. The best way to improve the structural condition of a sewer network is to replace failed pipe sections. Planning replacement works necessitates knowledge of the present structural state of the network and of the evolution of this state in the near future. Towards this goal, a

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Caswell, Joseph M. "Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses." International Letters of Chemistry, Physics and Astronomy 34 (May 2014): 55–63. http://dx.doi.org/10.18052/www.scipress.com/ilcpa.34.55.

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Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present stud

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Caswell, Joseph M. "Combination of Wavelet Analysis and Artificial Neural Networks Applied to Forecast of Daily Cosmic Ray Impulses." International Letters of Chemistry, Physics and Astronomy 34 (May 30, 2014): 55–63. http://dx.doi.org/10.56431/p-6mpq01.

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Artificial neural network modelling has proven incredibly effective in an impressively wide range of scientific disciplines. The combination of these various methods with wavelet decomposition signal processing has similarly proven to be a powerful development for statistical forecasting of a number of environmental processes. Space weather modelling and prediction has often been applied to forecasting of solar activity and that of the planetary magnetic field. However, prediction of cosmic ray impulses has seen little development in the context of neural network modelling. In the present stud

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Tarapata, Zbigniew. "Modelling and analysis of transportation networks using complex networks: Poland case study." Archives of Transport 36, no. 4 (2015): 55–65. http://dx.doi.org/10.5604/08669546.1185207.

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In the paper a theoretical bases and empirical results deal with analysis and modelling of transportation networks in Poland using complex networks have been presented. Properties of complex networks (Scale Free and Small World) and network's characteristic measures have been described. In this context, results of empirical researches connected with characteristics of passenger air links network, express railway links network (EuroCity and InterCity) and expressways/highways network in Poland have been given. For passenger air links network in Poland results are compared with the same networks

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Hillier, Michael, Florian Wellmann, Eric A. de Kemp, Boyan Brodaric, Ernst Schetselaar, and Karine Bédard. "GeoINR 1.0: an implicit neural network approach to three-dimensional geological modelling." Geoscientific Model Development 16, no. 23 (2023): 6987–7012. http://dx.doi.org/10.5194/gmd-16-6987-2023.

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Abstract. Implicit neural representation (INR) networks are emerging as a powerful framework for learning three-dimensional shape representations of complex objects. These networks can be used effectively to model three-dimensional geological structures from scattered point data, sampling geological interfaces, units, and structural orientations. The flexibility and scalability of these networks provide a potential framework for integrating many forms of geological data and knowledge that classical implicit methods cannot easily incorporate. We present an implicit three-dimensional geological

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Xiao, Lin, Fahui Wu, Dingcheng Yang, Tiankui Zhang, and Xiaoya Zhu. "Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks." Journal of Sensors 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/3831810.

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The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has similarity to random networks of small average path length. It can be utilized to optimize the energy e

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Mukhtar, F., Y. Kuznetsov, and P. Russer. "Network modelling with Brune's synthesis." Advances in Radio Science 9 (July 29, 2011): 91–94. http://dx.doi.org/10.5194/ars-9-91-2011.

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Abstract. Network modelling of general, lossy or lossless, one-port and symmetric two-port passive electromagnetic structures in systematic manner is presented. Rational function representation of the numerical data of Z- or Y-parameters is obtained with the use of Vector Fitting procedure. A systematic strategy for obtaining equivalent lumped element circuit from the rational function, applying Brune's circuit synthesis, is also presented.

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Manvelidze, A. B. "Air lines network modelling algorithm." Strategic decisions and risk management, no. 6 (February 13, 2018): 22–29. http://dx.doi.org/10.17747/2078-8886-2017-6-22-29.

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This analysis is dedicated to find out methods for setting of route networks where new aircraft can be effectively put into service. The conception of this analysis is based on the idea of so called connectivity principle for airports connected by passenger traffic with each other.For the passenger traffic analysis the author took passenger traffic data by federal districts starting from the Far East. Then consequently the data for Siberian, Ural, Wolga, Northwestern, Central, Southern and North Caucasian federal districts were analyzed. Passenger traffic to the Crimea was treated separately.

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Mahendra V, Manjunatha G S, Dr. Nagaraja G S, and Dr. Shushrutha K S. "Network Threat Detection and Modelling." International Journal of Emerging Science and Engineering 12, no. 12 (2024): 16–19. http://dx.doi.org/10.35940/ijese.h9629.12121124.

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Network threat detection and modelling are critical aspects of network security in an organization since the many devices connecting to the internet can be vulnerable. Network attacks are unauthorized actions on the digital assets within an organizational network. Malicious parties usually execute network attacks to alter, destroy, or steal private data. Perpetrators in network attacks tend to target network perimeters to gain access to internal systems. In this project the incoming traffic and outgoing network traffic is analyzed and from the several devices in an organization and security de

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Rahman, Muhammad Azizur, Algirdas Pakštas, and Frank Zhigang Wang. "Network modelling and simulation tools." Simulation Modelling Practice and Theory 17, no. 6 (2009): 1011–31. http://dx.doi.org/10.1016/j.simpat.2009.02.005.

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Smith, Stephen M., Karla L. Miller, Gholamreza Salimi-Khorshidi, et al. "Network modelling methods for FMRI." NeuroImage 54, no. 2 (2011): 875–91. http://dx.doi.org/10.1016/j.neuroimage.2010.08.063.

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Ho, Joshua W. K., and Michael A. Charleston. "Network modelling of gene regulation." Biophysical Reviews 3, no. 1 (2010): 1–13. http://dx.doi.org/10.1007/s12551-010-0041-4.

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Craven, B. D. "Modelling inventories in a network." Optimization Letters 1, no. 4 (2007): 401–6. http://dx.doi.org/10.1007/s11590-006-0040-3.

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Zhang, Shumin, Tianxia Jia, and Minhui Li. "Partial domination of network modelling." AIMS Mathematics 8, no. 10 (2023): 24225–32. http://dx.doi.org/10.3934/math.20231235.

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<abstract><p>Partial domination was proposed in 2017 on the basis of domination theory, which has good practical application background in communication network. Let $ G = (V, E) $ be a graph and $ \mathcal{F} $ be a family of graphs, a subset $ S\subseteq V $ is called an $ \mathcal{F} $-isolating set of $ G $ if $ G[V\backslash N_G[S]] $ does not contain $ F $ as a subgraph for all $ F\in\mathcal{F} $. The subset $ S $ is called an isolating set of $ G $ if $ \mathcal{F} = \{K_2\} $ and $ G[V\backslash N_G[S]] $ does not contain $ K_2 $ as a subgraph. The isolation number of $ G

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Mahendra, V. "Network Threat Detection and Modelling." International Journal of Emerging Science and Engineering (IJESE) 12, no. 12 (2024): 16–19. https://doi.org/10.35940/ijese.H9629.12121124.

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<strong>Abstract:</strong> Network threat detection and modelling are critical aspects of network security in an organization since the many devices connecting to the internet can be vulnerable. Network attacks are unauthorized actions on the digital assets within an organizational network. Malicious parties usually execute network attacks to alter, destroy, or steal private data. Perpetrators in network attacks tend to target network perimeters to gain access to internal systems. In this project the incoming traffic and outgoing network traffic is analyzed and from the several devices in an o

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Perestrelo, Sara, Maria C. Grácio, Nuno A. Ribeiro, and Luís M. Lopes. "Modelling Forest Fires Using Complex Networks." Mathematical and Computational Applications 26, no. 4 (2021): 68. http://dx.doi.org/10.3390/mca26040068.

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Forest fires have been a major threat to the environment throughout history. In order to mitigate its consequences, we present, in a first of a series of works, a mathematical model with the purpose of predicting fire spreading in a given land portion divided into patches, considering the area and the rate of spread of each patch as inputs. The rate of spread can be estimated from previous knowledge on fuel availability, weather and terrain conditions. We compute the time duration of the spreading process in a land patch in order to construct and parametrize a landscape network, using cellular

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Suárez Valencia, Erika, Víctor Bucheli, Roberto Zarama, and Ángel Garcia. "Collective intelligence: analysis and modelling." Kybernetes 44, no. 6/7 (2015): 1122–33. http://dx.doi.org/10.1108/k-11-2014-0245.

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Purpose – The purpose of this paper is to focus on the underpinning dynamics that explain collective intelligence. Design/methodology/approach – Collective intelligence can be understood as the capacity of a collective system to evolve toward higher order complexity through networks of individual capacities. The authors observed two collective systems as examples of the dynamic processes of complex networks – the wiki course PeSO at the Universidad de Los Andes, Bogotá, Colombia, and an agent-based model inspired by wiki systems. Findings – The results of the wiki course PeSO and the model are

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Sarkar, Nurul I., and Kashif Nisar. "Modelling and Performance Studies of ATM Networks Over Email & FTP." International Journal of Advanced Pervasive and Ubiquitous Computing 4, no. 2 (2012): 16–25. http://dx.doi.org/10.4018/japuc.2012040102.

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The Advanced Network Technologies is a research that investigates the technology(s) behind today’s modern networks and network infrastructures – one of these technologies being Asynchronous Transfer Mode (ATM); a technology commonly in place in networks all around the world today. Therefore, this paper focuses its attention on ATM. Dubbed “Modelling and Performance Studies of ATM Networks,” this research looks into the “impact of application segment length on the performance of an ATM network” and the “impact of traffic type data on the performance of an ATM network.” For this research, the au

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Martynenko, A. V., and O. N. Ie. "Modelling of natural development of interurban network of vehicular roads." Herald of the Ural State University of Railway Transport, no. 2 (2020): 4–12. http://dx.doi.org/10.20291/2079-0392-2020-2-4-12.

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The main peculiarities of transport network functioning and structure are the result of reciprocal influence of the network and its service area. Namely, current transport needs of the area are the key factor, affecting any specific decision on creating a new network element. However, transport needs of the area significantly changes in the course of time and certain management decisions are poorly coordinated and made in conditions of incomplete information on future network state. Therefore, transport networks have a variety of features of natural objects and their development can be regarde

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