Статті в журналах: "Network management models"

1

Šubrt, T. "Multiple criteria network models for project management." Agricultural Economics (Zemědělská ekonomika) 50, No. 2 (February 24, 2012): 71–76. http://dx.doi.org/10.17221/5169-agricecon.

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The aim of the paper is to present one possibility of how to model and solve a resource oriented critical path problem. As a starting point, a single criteria model for critical path finding is shortly mentioned. Lately, more criteria functions for this model are defined. If any project task uses more resources for its completion, its duration usually depends on only one of them – other resources are not fully used. In here defined multiple criteria approach, these dependencies are not assumed. Each criteria function is derived from a theoretical task duration based on a number of units of only one resource and on its importance. Using either linear programming model with aggregated criteria function or simple Excel calculation with Microsoft Project software support, a so-called compromise critical path can be found. On this path, some resources are overallocated and some are underallocated but the total sum of all underallocations and all overallocations is minimized. All resources are used as effectively as possible and the project is as short as possible too.

2

Pilz, Andreas, and Joachim Swoboda. "Network Management Information Models." AEU - International Journal of Electronics and Communications 58, no. 3 (January 2004): 165–71. http://dx.doi.org/10.1078/1434-8411-54100224.

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3

Vemuganti, R. R., M. Oblak, and A. Aggarwal. "Network Models for Fleet Management." Decision Sciences 20, no. 1 (March 1989): 182–97. http://dx.doi.org/10.1111/j.1540-5915.1989.tb01406.x.

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4

van Hemmen, L. J. G. T. "Models supporting the network management organization." International Journal of Network Management 10, no. 6 (2000): 299–314. http://dx.doi.org/10.1002/1099-1190(200011/12)10:6<299::aid-nem377>3.0.co;2-l.

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5

Chinneck, J. W., and R. H. H. Moll. "Processing network models for forest management." Omega 23, no. 5 (October 1995): 499–510. http://dx.doi.org/10.1016/0305-0483(95)00023-h.

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6

Szyrkowiec, Thomas, Achim Autenrieth, and Wolfgang Kellerer. "Optical Network Models and Their Application to Software-Defined Network Management." International Journal of Optics 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/5150219.

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Software-defined networking is finding its way into optical networks. Here, it promises a simplification and unification of network management for optical networks allowing automation of operational tasks despite the highly diverse and vendor-specific commercial systems and the complexity and analog nature of optical transmission. Common abstractions and interfaces are a fundamental component for software-defined optical networking. Currently, a number of models for optical networks are available. They all claim to provide open and vendor agnostic management of optical equipment. In this work, we survey and compare the most important models and propose an intent interface for creating virtual topologies which is integrated in the existing model ecosystem.

7

Van Wambeke, N., F. Armando, and A. Abdelkefi. "Models and Architecture for Autonomic Network Management." International Journal of Business Data Communications and Networking 5, no. 2 (April 2009): 35–51. http://dx.doi.org/10.4018/jbdcn.2009040103.

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8

Lopez de Vergaro, J. E., V. A. Villagra, J. I. Asensio, and J. Berrocat. "Ontologies: giving semantics to network management models." IEEE Network 17, no. 3 (May 2003): 15–21. http://dx.doi.org/10.1109/mnet.2003.1201472.

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9

Колесников, M. Kolesnikov, Киба, and M. Kiba. "Management in Social and Professional Networks." Administration 3, no. 3 (September 17, 2015): 68–72. http://dx.doi.org/10.12737/13340.

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The extended idea about “professional network” has been developed based on the concept of “social network”. The main features and tasks of professional social networks have been revealed. The task of management in social and professional networks which demanded formalization of criteria and models of their functioning has been set. Professional social network has been represented as a graph model. Known models of influence in social and professional networks have been considered and described in detail from the point of view of their analytical representation. The main features of professional networks influence on society and production have been revealed. Possibility for mass distribution of information by means of professional social networks has been analyzed. A mechanism for network agents’ opinion formation on the basis of authoritative opinion and a trust vector has been revealed.

10

Scott, Brenda M. "Analysis of Network and System Management Tasks." Proceedings of the Human Factors Society Annual Meeting 33, no. 4 (October 1989): 215–18. http://dx.doi.org/10.1177/154193128903300403.

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Network and system management tasks revolve around the activities necessary to establish and maintain data communication networks. This can include tasks such as: • monitoring the network hardware and software components for problems and performance information • identifying and correcting problems in the network • planning and making changes to the network. Task analysis was performed on data available from a customer case study database, (Beith, Moore, Pendley, and Percival, 1985). Models were developed for several network and system management tasks. This paper discusses one of the task models, problem determination, and presents recommendations for information structuring and task flow.

11

Smith, David K., and S. K. Gupta. "Linear Programming and Network Models." Journal of the Operational Research Society 38, no. 6 (June 1987): 561. http://dx.doi.org/10.2307/2582771.

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12

Smith, David K. "Linear Programming and Network Models." Journal of the Operational Research Society 38, no. 6 (June 1987): 561–62. http://dx.doi.org/10.1057/jors.1987.95.

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13

Reum, Jonathan C. P., Christopher R. Kelble, Chris J. Harvey, Robert P. Wildermuth, Neda Trifonova, Sean M. Lucey, P. Sean McDonald, and Howard Townsend. "Network approaches for formalizing conceptual models in ecosystem-based management." ICES Journal of Marine Science 78, no. 10 (October 30, 2021): 3674–86. http://dx.doi.org/10.1093/icesjms/fsab211.

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Abstract Qualitative Network Models (QNMs), Fuzzy Cognitive Maps (FCMs), and Bayesian Belief Networks (BBNs) have been proposed as methods to formalize conceptual models of social–ecological systems and project system responses to management interventions or environmental change. To explore how these different methods might influence conclusions about system dynamics, we assembled conceptual models representing three different coastal systems, adapted them to the network approaches, and evaluated outcomes under scenarios representing increased fishing effort and environmental warming. The sign of projected change was the same across the three network models for 31–60% of system variables on average. Pairwise agreement between network models was higher, ranging from 33 to 92%; average levels of similarity were comparable between network pairs. Agreement measures based on both the sign and strength of change were substantially worse for all model comparisons. These general patterns were similar across systems and scenarios. Different outcomes between models led to different inferences regarding trade-offs under the scenarios. We recommend deployment of all three methods, when feasible, to better characterize structural uncertainty and leverage insights gained under one framework to inform the others. Improvements in precision will require model refinement through data integration and model validation.

14

Bunimovich, L. A., D. C. Smith, and B. Z. Webb. "Specialization Models of Network Growth." Journal of Complex Networks 7, no. 3 (September 17, 2018): 375–92. http://dx.doi.org/10.1093/comnet/cny024.

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AbstractOne of the most important features observed in real networks is that, as a network’s topology evolves so does the network’s ability to perform various complex tasks. To explain this, it has also been observed that as a network grows certain subnetworks begin to specialize the function(s) they perform. Herein, we introduce a class of models of network growth based on this notion of specialization and show that as a network is specialized using this method its topology becomes increasingly sparse, modular and hierarchical, each of which are important properties observed in real networks. This procedure is also highly flexible in that a network can be specialized over any subset of its elements. This flexibility allows those studying specific networks the ability to search for mechanisms that describe their growth. For example, we find that by randomly selecting these elements a network’s topology acquires some of the most well-known properties of real networks including the small-world property, disassortativity and a right-skewed degree distribution. Beyond this, we show how this model can be used to generate networks with real-world like clustering coefficients and power-law degree distributions, respectively. As far as the authors know, this is the first such class of models that can create an increasingly modular and hierarchical network topology with these properties.

15

Athanasenas, A. "Traffic simulation models for rural road network management." Transportation Research Part E: Logistics and Transportation Review 33, no. 3 (September 1997): 233–43. http://dx.doi.org/10.1016/s1366-5545(97)00020-3.

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16

Mikheeva, T. I., A. A. Osmushin, S. V. Mikheev, and O. K. Golovnin. "GIS-based models for transport network emergency management." Journal of Physics: Conference Series 1353 (November 2019): 012009. http://dx.doi.org/10.1088/1742-6596/1353/1/012009.

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17

Chemouil, P., and J. Filipiak. "Supporting network management with real-time traffic models." IEEE Journal on Selected Areas in Communications 9, no. 2 (1991): 151–56. http://dx.doi.org/10.1109/49.68441.

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18

Jorjani, S., and BW Lamar. "Cash flow management network models with quantity discounting." Omega 22, no. 2 (March 1994): 149–55. http://dx.doi.org/10.1016/0305-0483(94)90075-2.

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19

Kazuya Odagiri, Giuseppe De Marco, Rihito Yaegashi, Masaharu Tadauchi, and Naohiro Ishii. "The Processing Workload Evaluation in two Network Management Models of IP Networks." Journal of Convergence Information Technology 4, no. 3 (September 30, 2009): 7–16. http://dx.doi.org/10.4156/jcit.vol4.issue3.1.

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20

Gupta, Raj Kumar, and K. L. Jaiswal. "STUDY AND ANALYSIS OF GRAPHS AND MODELS BASED INTERCONNECTION NETWORK SYSTEM." International Journal of Engineering Technologies and Management Research 8, no. 1 (February 17, 2021): 53–56. http://dx.doi.org/10.29121/ijetmr.v8.i1.2021.872.

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The topology of network connections has become a vital step in the implementation of all conventional network networks. Cube architecture is one of the key aspects of network integration that explore and create networks of cubes. They praise and work together to complete all network management tasks. However, in this article, we will focus on the error handling function.

21

Tso, Michael. "Network Flow Models in Image Processing." Journal of the Operational Research Society 37, no. 1 (January 1986): 31. http://dx.doi.org/10.2307/2582544.

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22

Tso, Michael. "Network Flow Models in Image Processing." Journal of the Operational Research Society 37, no. 1 (January 1986): 31–34. http://dx.doi.org/10.1057/jors.1986.5.

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23

Clausen, Jens. "Linear programming and network models." European Journal of Operational Research 28, no. 2 (February 1987): 237–38. http://dx.doi.org/10.1016/0377-2217(87)90232-3.

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24

Gong, Qiguo, K. K. Lai, and Shouyang Wang. "Supply chain networks: Closed Jackson network models and properties." International Journal of Production Economics 113, no. 2 (June 2008): 567–74. http://dx.doi.org/10.1016/j.ijpe.2007.10.013.

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25

Gökgöz, Fazil, and Fahrettin Filiz. "Electricity price forecasting in Turkey with artificial neural network models." Investment Management and Financial Innovations 13, no. 3 (September 23, 2016): 150–58. http://dx.doi.org/10.21511/imfi.13(3-1).2016.01.

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The electricity market has experienced significant changes towards deregulation with the aim of improving economic efficiency. The electricity pricing is a major consideration for consumers and generation companies in deregulated electric markets, so that offering the right price for electricity has become more important. Various methods and ideas have been tried for electricity price forecasting. Artificial neural networks have received much attention with its nonlinear property and many papers have reported successful experiments with them. This paper introduces artificial neural network models for day-ahead electricity market in Turkey. Using gradient descent, gradient descent with momentum, Broydan, Fletcher, Goldfarb and Shanno (BFGS) and Levenberg-Marquardt algorithm with different number of neuron and transfer functions, 400 different models are created. Performances of different models are compared according to their Mean Absolute Percentage (MAPE) values; the most successful models MAPE value is observed as 9.76%. Keywords: electricity price forecasting, neural networks, day-ahead electricity market, Turkey. JEL Classification: C02, C13, C45, C53

26

Sosnowska, Alicja, and Stanisław Łobejko. "Science and technology park management models." Kwartalnik Nauk o Przedsiębiorstwie 45, no. 4 (December 21, 2017): 43–54. http://dx.doi.org/10.5604/01.3001.0010.7458.

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The article presents four models of science and technology park management, built on the basis of the results of the analysis of 15 selected parks in the world. It discusses the basic functions of the park, advantages and disadvantages of each of the models, indicating also the possibilities of their further development. It has also been related to the Polish conditions, which proved that two models are most suitable for Poland: corporate and network. The corporate model, based on an active cooperation of a technical university with public and private entities, in the opinion of the Authors, is the most appropriate to be applied in the case of science and technology parks created near technical universities. An alternative solution to the corporate one is a network model, allowing for creation of sci-ence and technology parks with scattered spatial structure.

27

Talebi, Kayhan, and James Macgregor Smith. "Stochastic network evacuation models." Computers & Operations Research 12, no. 6 (January 1985): 559–77. http://dx.doi.org/10.1016/0305-0548(85)90054-1.

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28

Adami, Vivian Sebben, and Jorge Renato Verschoore. "Implications of Network Relations for the Governance of Complex Projects." Project Management Journal 49, no. 2 (April 2018): 71–88. http://dx.doi.org/10.1177/875697281804900205.

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Our article aims to answer the call for studies on new perspectives of complex projects and their governance. We adopt the social network approach to investigate the implications of network relations for the governance of project networks. We analyze quantitative and qualitative data following two theoretical models: flow and coordination. Our results show how the supply, contractual, and information networks influence the governance of project networks. We contribute to the literature explaining the dependence of the project network governance to network relations. It is necessary to use different theoretical models to analyze the coordination and control of complex project networks.

29

Vadakpat, Govindarajan, Shelley Stoffels, and Karen Dixon. "Road User Cost Models for Network-Level Pavement Management." Transportation Research Record: Journal of the Transportation Research Board 1699, no. 1 (January 2000): 49–57. http://dx.doi.org/10.3141/1699-07.

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Life-cycle cost analysis (LCCA) of pavements is a process for evaluating total economic worth of a usable project segment by analyzing initial costs and discounted future costs, such as those for maintenance, reconstruction, rehabilitation, and resurfacing. One of the most important ingredients in the LCCA process, at either a network level or a project level, is the determination of road user cost (RUC) during maintenance and rehabilitation operations. RUC models are also important in contracting strategies, which take into account time for a project to be completed for award and payment. Methods used to date to determine RUC are exclusively analytical in nature. Microscopic estimates of traffic are used to determine RUC. CORSIM, a microscopic traffic simulation program developed by FHWA, was used for this research. Models for additional travel time, added fuel consumption, and RUC for standard two-to-one lane closure scenarios are presented.

30

ZHOU, Ning, and Huan-yan QIAN. "Ontology-based mapping approach for network management information models." Journal of Computer Applications 30, no. 10 (December 28, 2010): 2838–42. http://dx.doi.org/10.3724/sp.j.1087.2010.02838.

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31

Chandra, Charu, and Alexander V. Smirnov. "Supply Chain Network E-Management Based on Constraints Models." Journal of Decision Systems 11, no. 2 (January 2002): 205–30. http://dx.doi.org/10.3166/jds.11.205-230.

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32

Manley, A., and C. Thomas. "Evolution of TMN network object models for broadband management." IEEE Communications Magazine 35, no. 10 (1997): 60–65. http://dx.doi.org/10.1109/35.623987.

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33

Yudov, Alexander, Vadim Trofimov, Dmitry Andreev, Iuliia Bruttan, and Anton Verteshev. "STRUCTURAL MANAGEMENT PARADIGM IN THE MODELS OF NETWORK ORGANIZATIONS." ENVIRONMENT. TECHNOLOGIES. RESOURCES. Proceedings of the International Scientific and Practical Conference 2 (June 17, 2021): 194–98. http://dx.doi.org/10.17770/etr2021vol2.6631.

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Development problem of network organizations’ modelling methods is considered in this article. It is characterized with two fundamental directions that are the basis of the scientific approach. The authors offer one more aspect of modelling – taking into account the influence on the object’s structure and the produced products of external messages that carry structural rules, i. e. the rules needed for similar objects modelling.

34

Yang, Jidong, Jian John Lu, Manjriker Gunaratne, and Qiaojun Xiang. "Forecasting Overall Pavement Condition with Neural Networks: Application on Florida Highway Network." Transportation Research Record: Journal of the Transportation Research Board 1853, no. 1 (January 2003): 3–12. http://dx.doi.org/10.3141/1853-01.

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Timely identification of undesirable crack, ride, and rut conditions is a critical issue in pavement management systems at the network level. The overall pavement surface condition is determined by these individual pavement surface conditions. A research project was carried out to implement an overall methodology for pavement condition prediction that uses artificial neural networks (ANNs). In the research, three ANN models were developed to predict the three key indices—crack rating, ride rating, and rut rating—used by the Florida Department of Transportation (FDOT) for pavement evaluation. The ANN models for each index were trained and tested by using the FDOT pavement condition database. In addition to the three key indices, FDOT uses a composite index called pavement condition rating (PCR), which is the minimum of the three key indices, to summarize overall pavement surface condition for pavement management. PCR is forecast with a combination of the three ANN models. Results of the research suggest that the ANN models are more accurate than the traditional regression models. These ANN models can be expected to have a significant effect on FDOT's pavement management system.

35

Randolph, Dennis A. "Application of Neural Network Technology to Highway Maintenance Management." Transportation Research Record: Journal of the Transportation Research Board 1533, no. 1 (January 1996): 3–10. http://dx.doi.org/10.1177/0361198196153300101.

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Modern maintenance management systems provide detailed information for managers. However, this information and the detailed descriptions of work and production do not reflect the influence that people have on each other. Team composition is critical to the attainment of productivity goals, but managers must depend upon intuitive skills to make team assignments. The objective of this work was to develop a system that managers could use to assist them in making team assignments. Neural network techniques were used to develop predictive models for team productivity. Historic data on team composition and productivity were used to train neural networks, and then the trained networks used to estimate production. Team productivity tables can be developed using the trained neural networks, and managers can use these tables to guide the assignment of workers to crews. Neural networks can be used to replace the intuitive skills that managers often lack. Because neural networks use historic data to train, the predictive models produce intuitively comfortable results. Moreover, because neural network programs take advantage of modern computer power and use brute force to generate results, the limits of other statistical techniques are avoided.

36

Chang, Tsung-Sheng, Kaoru Tone, and Quanling Wei. "Ownership-specified network DEA models." Annals of Operations Research 214, no. 1 (August 30, 2011): 73–98. http://dx.doi.org/10.1007/s10479-011-0949-5.

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37

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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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.

38

Turek, Tomasz. "Models of regional business spatial community management." Online Journal of Applied Knowledge Management 4, no. 1 (May 1, 2016): 17–28. http://dx.doi.org/10.36965/ojakm.2016.4(1)17-28.

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Online communities are no longer the domain of people. Due to changes in economy, the free flow of capital and people, and the processes of globalization, more and more often enterprises appear in communities. This applies in particular to the organizations based on the resources of information and knowledge. Relations of this type are called business communities. A specific form of this type of relation is projects undertaken within geographical areas: cities, states, and provinces. Due to the bonding agent - the geographical area - they are based on spatial information GIS systems. Projects arising this way can be called Regional Business Spatial Community - RBSC. In RBSC it is possible to perceive the features of network organizations and virtual organizations (VO). Network organization features are the result of geographical dispersion, established relations, common goals, and mutual synergic replenishment. VO features are a result of the role of information resources, temporariness, fuzziness, and the ability to reconfigure. Due to the characteristics of network organization and VO, RBSC management becomes a problem. The question is who has to make decisions and on what conditions in such projects. Possible scenarios are: democratic governance, the establishment of common unit coordinating and managing, ad-hoc flexible management, or the appointment of the subject responsible for the management.

39

Wei, Q. L., and T.-S. Chang. "Optimal system design series-network DEA models." Journal of the Operational Research Society 62, no. 6 (June 2011): 1109–19. http://dx.doi.org/10.1057/jors.2010.45.

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40

Agieva, Movlatkhan T., Alexei V. Korolev, and Guennady A. Ougolnitsky. "Game theoretic models of sustainable management in marketing networks." Contributions to Game Theory and Management 13 (2020): 24–56. http://dx.doi.org/10.21638/11701/spbu31.2020.03.

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Difference and differential Stackelberg games of opinion control on marketing networks are considered. The principal allocates financial resources to the firms for marketing purposes. It is supposed that the structure of a target audience described by a weighted directed graph is already determined in the stage of network analysis, and marketing control actions are applied only to the members of strong subgroups (opinion leaders). Conditions of homeostasis (phase constraints) which reflect the requirements of sustainable management are introduced additionally. The Stackelberg equilibria are found analytically. It is shown that the interests of the principal and the firms are completely compatible.

41

Wang, Ruxian, and Zizhuo Wang. "Consumer Choice Models with Endogenous Network Effects." Management Science 63, no. 11 (November 2017): 3944–60. http://dx.doi.org/10.1287/mnsc.2016.2520.

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42

Hill, Tim, Marcus O'Connor, and William Remus. "Neural Network Models for Time Series Forecasts." Management Science 42, no. 7 (July 1996): 1082–92. http://dx.doi.org/10.1287/mnsc.42.7.1082.

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43

Spragins, John. "Local-area network reliability models." Quality and Reliability Engineering International 10, no. 3 (1994): 201–16. http://dx.doi.org/10.1002/qre.4680100311.

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44

Smith, G. Stevenson. "Management models for international cybercrime." Journal of Financial Crime 22, no. 1 (January 5, 2015): 104–25. http://dx.doi.org/10.1108/jfc-09-2013-0051.

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Purpose – The purpose of this paper is to identify how the management structure of cybercriminals has changed and will continue to be revised in the future as their criminal business models are modified. In the early days of hacktivism, a distinction was made between a “hacker” and a “cracker”. The hacker was considered someone who was interested in the vulnerabilities in a computer system, but they were not out to exploit these vulnerabilities for illicit gains. Today, this is no longer true, as loosely coordinated gangs of computer hackers exploit vulnerabilities of financial institutions and the public to steal and transfer money across borders without difficulty. Design/methodology/approach – The paper reviews legal cases dealing with the computer theft of assets from financial institutions and individuals. The focus is on external exploits of hackers not on employee’s theft of assets. It explores the management structure used by cybercriminals who have been caught and prosecuted by legal authorities in the USA and other countries. The paper discusses how this management structure has evolved from older traditional crime business models based on “family” relationships to morphing criminal gangs based in Russia, the Ukraine and other locations almost untouchable by the US legal authorities. These new criminal networks are based on knowledge relationships and quickly disappearing network connections. The paper concludes with a discussion regarding the management structure cybercriminals will follow in the future, as they continue their criminal activities. Findings – The study provides indications of a trend toward more complex management and organizational structures among cybergangs. Originality/value – Although there are many annual studies identifying the growth of cybercrime and the types of attacks being made, but there is not even a single study that shows how the cybercrime business model has changed over the past 20 years. From that perspective, the paper provides information of a changing and more effective business model for cyberattacks.

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Kambil, Ajit. "Purposeful abstractions: thoughts on creating business network models." Journal of Business Strategy 29, no. 1 (January 4, 2008): 52–54. http://dx.doi.org/10.1108/02756660810845723.

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Jain, Bharat A., and Barin N. Nag. "Performance Evaluation of Neural Network Decision Models." Journal of Management Information Systems 14, no. 2 (September 1997): 201–16. http://dx.doi.org/10.1080/07421222.1997.11518171.

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Hong, Sheng, Xiaojie Zhang, Juxing Zhu, Tingdi Zhao, and Baoqing Wang. "Suppressing failure cascades in interconnected networks: Considering capacity allocation pattern and load redistribution." Modern Physics Letters B 30, no. 05 (February 20, 2016): 1650049. http://dx.doi.org/10.1142/s0217984916500494.

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In the modern society, most networked critical infrastructures are coupled together and can be modeled as interconnected networks. Cascading failures caused by overload in interconnected networks are extremely complicated and still lack of corresponding researches. It is crucial to explore a systematic design and load management method for complex interconnected network. In this paper, a probabilistic heterogeneous capacity allocation model and a preventive load redistribution model are firstly proposed. Based on the novel models failure cascades in interconnected networks are studied in detail. Numerical simulations and analysis results show that robustness of interconnected network depends on the network topology, capacity allocation pattern and load management strategy. The novel models have guiding significance for the design and optimal management of robust interconnected infrastructure networks.

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Attoh-Okine, Nii O. "Modeling incremental pavement roughness using functional network." Canadian Journal of Civil Engineering 32, no. 5 (October 1, 2005): 899–905. http://dx.doi.org/10.1139/l05-050.

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Incremental roughness prediction is a critical component of decision making of any pavement management systems, therefore, proper estimation is of paramount importance. This paper presents the application of functional equations and networks to incremental roughness prediction of flexible pavement. In the functional networks, neuron functions are multivariate and multiargumentative. Functional equations form the basis of functional networks, therefore, established theorem in functional equations are easily applicable in the analysis. The model is developed from validated set of incremental and interactive pavement distress functions in the highway design and maintenance standard models (HDM). The models proposed and developed are intended for use in infrastructure management (pavement) applications and as a performance model for pavement design. The paper presents a computational procedure of the functional network using the serial associative functional network. The functional equation and networks approach use the domain knowledge and data for developing the roughness models.Key words: pavement roughness, functional equation, functional networks, neural networks, pavement management.

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Cao, Yuan, Lin Yang, Zom Bo Fu, and Feng Yang. "Identity Management Architecture: Paradigms and Models." Applied Mechanics and Materials 40-41 (November 2010): 647–51. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.647.

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This paper provides an overview of identity management architecture from the viewpoint of paradigms and models. The definition of identity management architecture has been discussed, paradigms are classified by the development stage and core design principle transmission of the architecture which include network centric paradigm, service centric paradigm, and user centric paradigm; models are grouped by components varying and functions changing to isolated model, centralized model, and federated model. These paradigms and models have no collisions among them for they are views of identity management from different viewpoint.

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Kalish, Yuval. "Stochastic Actor-Oriented Models for the Co-Evolution of Networks and Behavior: An Introduction and Tutorial." Organizational Research Methods 23, no. 3 (January 23, 2019): 511–34. http://dx.doi.org/10.1177/1094428118825300.

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Stochastic actor-oriented (SAO) models are a family of models for network dynamics that enable researchers to test multiple, often competing explanations for network change and estimate the extent and relative power of various influences on network evolution. SAO models for the co-evolution of network ties and actor behavior, the most comprehensive category of SAO models, examine how networks and actor attributes—their behavior, performance, or attitudes—influence each other over time. While these models have been widely used in the social sciences, and particularly in educational settings, their use in organizational scholarship has been extremely limited. This paper provides a layperson introduction to SAO models for the co-evolution of networks and behavior and the types of research questions they can address. The models and their underpinnings are explained in nonmathematical terms, and theoretical explanations are supported by a concrete, detailed example that includes step-by-step model building and hypothesis testing, alongside an R script.

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