Literatura académica sobre el tema "Complex Networks of treaties"
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Artículos de revistas sobre el tema "Complex Networks of treaties"
Denters, Erik y Tarcisio Gazzini. "The Role of African Regional Organizations in the Promotion and Protection of Foreign Investment". Journal of World Investment & Trade 18, n.º 3 (26 de diciembre de 2017): 449–92. http://dx.doi.org/10.1163/22119000-12340048.
Texto completoFang, Xinli, Qiang Yang y Wenjun Yan. "Outer Synchronization between Complex Networks with Nonlinear Time-Delay Characteristics and Time-Varying Topological Structures". Mathematical Problems in Engineering 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/437673.
Texto completoLiu, Xiangrong, Zengyan Hong, Juan Liu, Yuan Lin, Alfonso Rodríguez-Patón, Quan Zou y Xiangxiang Zeng. "Computational methods for identifying the critical nodes in biological networks". Briefings in Bioinformatics 21, n.º 2 (12 de febrero de 2019): 486–97. http://dx.doi.org/10.1093/bib/bbz011.
Texto completoSMITH, REGINALD D. "THE DYNAMICS OF INTERNET TRAFFIC: SELF-SIMILARITY, SELF-ORGANIZATION, AND COMPLEX PHENOMENA". Advances in Complex Systems 14, n.º 06 (diciembre de 2011): 905–49. http://dx.doi.org/10.1142/s0219525911003451.
Texto completoSacco, Pier Luigi, Alex Arenas y Manlio De Domenico. "The Resilience of the Multirelational Structure of Geopolitical Treaties is Critically Linked to Past Colonial World Order and Offshore Fiscal Havens". Complexity 2023 (7 de enero de 2023): 1–9. http://dx.doi.org/10.1155/2023/5280604.
Texto completoWiley, David J., Ilona Juan, Hao Le, Xiaodong Cai, Lisa Baumbach, Christine Beattie y Gennaro D'Urso. "Yeast Augmented Network Analysis (YANA): a new systems approach to identify therapeutic targets for human genetic diseases". F1000Research 3 (2 de junio de 2014): 121. http://dx.doi.org/10.12688/f1000research.4188.1.
Texto completoArel-Bundock, Vincent. "The Unintended Consequences of Bilateralism: Treaty Shopping and International Tax Policy". International Organization 71, n.º 2 (2017): 349–71. http://dx.doi.org/10.1017/s0020818317000108.
Texto completoBorschberg, Peter. "Luso-Johor-Dutch Relations in the Straits of Malacca and Singapore, c. 1600-1623". Itinerario 28, n.º 2 (julio de 2004): 15–43. http://dx.doi.org/10.1017/s0165115300019471.
Texto completoZHENG DA-FANG, SHEN SHUN-QING y TAO RUI-BAO. "AN EXTENDED GENERATING FUNCTION TECHNIQUE FOR TREATING RANDOM WALKS ON COMPLEX NETWORKS". Acta Physica Sinica 37, n.º 11 (1988): 1823. http://dx.doi.org/10.7498/aps.37.1823.
Texto completoKim, Rakhyun E. "Is Global Governance Fragmented, Polycentric, or Complex? The State of the Art of the Network Approach". International Studies Review 22, n.º 4 (18 de septiembre de 2019): 903–31. http://dx.doi.org/10.1093/isr/viz052.
Texto completoTesis sobre el tema "Complex Networks of treaties"
Sanatkar, Mohammad Reza. "Epidemics on complex networks". Thesis, Kansas State University, 2012. http://hdl.handle.net/2097/14097.
Texto completoDepartment of Electrical and Computer Engineering
Karen Garrett
Bala Natarajan
Caterina Scoglio
In this thesis, we propose a statistical model to predict disease dispersal in dynamic networks. We model the process of disease spreading using discrete time Markov chain. In this case, the vector of probability of infection is the state vector and every element of the state vector is a continuous variable between zero and one. In discrete time Markov chains, state probability vectors in each time step depends on state probability vector in the previous time step and one step transition probability matrix. The transition probability matrix can be time variant or time invariant. If this matrix’s elements are functions of elements of vector state probability in previous step, the corresponding Markov chain is non linear dynamical system. However, if those elements are independent of vector state probability, the corresponding Markov chain is a linear dynamical system. We especially focus on the dispersal of soybean rust. In our problem, we have a network of US counties and we aim at predicting that which counties are more likely to get infected by soybean rust during a year based on observations of soybean rust up to that time as well as corresponding observations to previous years. Other data such as soybean and kudzu densities in each county, daily wind data, and distance between counties helps us to build the model. The rapid growth in the number of Internet users in recent years has led malware generators to exploit this potential to attack computer users around the word. Internet users are frequent targets of malicious software every day. The ability of malware to exploit the infrastructures of networks for propagation determines how detrimental they can be to the network’s security. Malicious software can make large outbreaks if they are able to exploit the structure of the Internet and interactions between users to propagate. Epidemics typically start with some initial infected nodes. Infected nodes can cause their healthy neighbors to become infected with some probability. With time and in some cases with external intervention, infected nodes can be cured and go back to a healthy state. The study of epidemic dispersals on networks aims at explaining how epidemics evolve and spread in networks. One of the most interesting questions regarding an epidemic spread in a network is whether the epidemic dies out or results in a massive outbreak. Epidemic threshold is a parameter that addresses this question by considering both the network topology and epidemic strength.
Venkatesan, Vaidehi. "Cuisines as Complex Networks". University of Cincinnati / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321969310.
Texto completoGonçalves, Bruno Miguel Tavares. "Topology of complex networks". Master's thesis, Universidade de Aveiro, 2004. http://hdl.handle.net/10773/16685.
Texto completoThe study of connectivity correlations between nodes has been somewhat neglected in the study of Complex Networks. We try to correct this by using the correlation function, combined with the concept of shell to calculate the connectivity distribution, P(d)(k) and the average connectivity for the neighbours,
O estudo das correlações de conectividade entre nodos tem sido algo negligenciado no estudo de Redes Complexas. Nós tentamos alterar esta situação usando funções de correlação em conjunto com o concenito de camada para calcular a distribuição de conectividades P(d)(k) e a conectividade média dos vizinhos
Buzzanca, Marco. "Sociality in Complex Networks". Doctoral thesis, Università di Catania, 2018. http://hdl.handle.net/10761/3766.
Texto completoMarchese, Emiliano. "Optimizing complex networks models". Thesis, IMT Alti Studi Lucca, 2022. http://e-theses.imtlucca.it/356/1/Marchese_phdthesis.pdf.
Texto completoTrusina, Ala. "Complex Networks : Structure, Function , Evolution". Doctoral thesis, Umeå University, Physics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-608.
Texto completoA 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.
Iyer, Swami. "Evolutionary dynamics on complex networks". Thesis, University of Massachusetts Boston, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3564666.
Texto completoMany complex systems such as the Internet can be represented as networks, with vertices denoting the constituent components of the systems and edges denoting the patterns of interactions among the components. In this thesis, we are interested in how the structural properties of a network, such as its average degree, degree distribution, clustering, and homophily affect the processes that take place on it. In the first part of the thesis we focus on evolutionary game theory models for studying the evolution of cooperation in a population of predominantly selfish individuals. In the second part we turn our attention to an evolutionary model of disease dynamics and the impact of vaccination on the spread of infection. Throughout the thesis we use a network as an abstraction for a population, with vertices representing individuals in the population and edges specifying who can interact with whom. We analyze our models for a well-mixed population, i.e., an infinite population with random mixing, and compare the theoretical results with those obtained from computer simulations on model and empirical networks.
Taylor, Alan J. "Computational tools for complex networks". Thesis, University of Strathclyde, 2009. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=12414.
Texto completoCooper, Kathryn. "Complex Networks : Similarity and Dynamics". Thesis, Imperial College London, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516486.
Texto completoRAMOS, MARLON FERREIRA. "OPINION DYNAMICS IN COMPLEX NETWORKS". PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=26418@1.
Texto completoCOORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
FUNDAÇÃO DE APOIO À PESQUISA DO ESTADO DO RIO DE JANEIRO
BOLSA NOTA 10
Esta tese aborda diversos problemas que podem ser tratados mediante modelos de dinâmica de opiniões, segundo os quais os indivíduos, conectados de acordo com redes complexas, interagem mediante regras que moldam as preferências e o posicionamento desses indivíduos com relação a uma determinada questão. A metodologia utilizada para investigar os padrões emergentes dessas interações consiste na utilização de diversas técnicas da física estatística. A tese está organizada em torno de quatro problemas distintos, com uma questão particular a ser respondida em cada caso, buscando sempre a validação empírica dos resultados teóricos e computacionais. No primeiro trabalho, é respondida a seguinte questão básica sobre propriedades da rede que podem ter impacto sobre os processos de propagação: quais são os valores típicos das distâncias, coeficiente de aglomeração e outras grandezas estruturais da rede, quando considerado o ensemble de redes aleatórias com uma assortatividade fixa? No segundo trabalho, investigamos os padrões que surgem na avaliação de filmes, considerando como fonte o IMDb (Internet Movie Database). Encontramos que a distribuição de votos apresenta um comportamento livre de escala com um expoente muito próximo de 3/2. Curiosamente, esse padrão é robusto, independente de atributos dos filmes como nota média, idade ou gênero. A análise empírica aponta para um mecanismo de propagação de adoções simples, que gera uma dinâmica de avalanches de campo médio. No terceiro trabalho, abordamos o problema de múltiplas escolhas por meio de um modelo que inclui a possibilidade de indecisão e onde as escolhas dos indivíduos evoluem segundo uma regra de pluralidade. Mostramos que essa dinâmica em redes com a propriedade de mundo pequeno produz diferentes estados estacionários realísticos, que dependem do número de alternativas e da distribuição de graus: consenso, distribuição de adoções larga similar à reais e situações onde a indecisão predomina, quando o número de alternativas é suficientemente grande. Por último, investigamos o surgimento de posições extremas na sociedade, mediante pesquisas em uma ampla gama de questões. O aumento de atitudes extremas tem como precursor uma relação não linear entre a fração de extremistas e a de moderados. Propomos um modelo, com regras de ativação baseadas na teimosia dos indivíduos, que permite interpretar o início da não linearidade em termos de uma transição abrupta do tipo percolação de inicialização onde acontecem cascatas de extremismo. Como conclusão geral, destacamos que esta tese ilustra como os modelos de opinião, aliados às enormes bases de dados, fornecem resultados com poder de interpretação e predição dos padrões empíricos.
This thesis addresses several problems that can be treated through models of opinion dynamics, according to which individuals, connected according to complex networks, interact through rules that shape their preferences and opinions in relation to a particular issue. The methodology used to investigate the patterns that emerge from those interactions relies on the use of various techniques of statistical physics. The thesis is organized around four distinct problems, with a particular question to be answered in each case, always looking for empirical validation of the theoretical and computational results. In the first work, it is answered the following basic question about network properties that can have impact on the spreading processes: what are the typical values of the distances, clustering coefficient and other structural quantities, when considering the ensemble of random networks with fix assortativity? In the second study, we investigated the patterns that emerge in the ratings of films, considering as source IMDb (Internet Movie Database). We found that the distribution of votes has a scale-free behavior with a exponent close to 3/2. Interestingly, this pattern is robust, independently of movie attributes such as average note, age or gender. The empirical analysis points to a simple mechanism of adoption propagation, that generates mean-field avalanches. In the third study, we discuss the problem of multiple choices by means of a model which includes the possibility of indecision and where the choices of individuals evolve according to a plurality rule. We show that this dynamics on top of networks with the small-world property produces different stationary states that depend on the number of alternatives and on the degree distribution: consensus, wide adoption distributions similar to actual ones and situations where indecision prevails when the number of alternatives is large enough. Finally, we investigate the appearance of extreme positions in society, through the polls on a wide variety of questions. The increase of extreme opinions has as precursor a non-linear relationship between the fraction of extremists and that of moderates. We propose a model with activation rules, based on the stubbornness of the individuals, which enables interpreting the beginning of the non-linearity in terms of an abrupt transition of the class of bootstrap percolation, where activation cascades occur. As a general conclusion, we emphasize that this thesis illustrates how opinion models, combined with huge databases, provide results with power of interpretation and prediction of empirical patterns.
Libros sobre el tema "Complex Networks of treaties"
da F. Costa, Luciano, Alexandre Evsukoff, Giuseppe Mangioni y Ronaldo Menezes, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25501-4.
Texto completoMenezes, Ronaldo, Alexandre Evsukoff y Marta C. González, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-30287-9.
Texto completoFortunato, Santo, Giuseppe Mangioni, Ronaldo Menezes y Vincenzo Nicosia, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01206-8.
Texto completoBen-Naim, Eli, Hans Frauenfelder y Zoltan Toroczkai, eds. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b98716.
Texto completoMenezes, Ronaldo. Complex Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Buscar texto completoTeixeira, Andreia Sofia, Diogo Pacheco, Marcos Oliveira, Hugo Barbosa, Bruno Gonçalves y Ronaldo Menezes, eds. Complex Networks XII. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81854-8.
Texto completoContucci, Pierluigi, Ronaldo Menezes, Andrea Omicini y Julia Poncela-Casasnovas, eds. Complex Networks V. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05401-8.
Texto completoCornelius, Sean P., Clara Granell Martorell, Jesús Gómez-Gardeñes y Bruno Gonçalves, eds. Complex Networks X. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14459-3.
Texto completoGhoshal, Gourab, Julia Poncela-Casasnovas y Robert Tolksdorf, eds. Complex Networks IV. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36844-8.
Texto completoGöknar, İzzet Cem y Levent Sevgi, eds. Complex Computing-Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-30636-6.
Texto completoCapítulos de libros sobre el tema "Complex Networks of treaties"
Arora, Sanjeev, Summers Kalishman, Denise Dion, Karla Thornton, Glen Murata, Connie Fassler, Steven M. Jenkusky et al. "Knowledge Networks for Treating Complex Diseases in Remote, Rural, and Underserved Communities". En Learning Trajectories, Innovation and Identity for Professional Development, 47–70. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1724-4_3.
Texto completoSilva, Thiago Christiano y Liang Zhao. "Complex Networks". En Machine Learning in Complex Networks, 15–70. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-17290-3_2.
Texto completoRubido, Nicolás. "Complex Networks". En Energy Transmission and Synchronization in Complex Networks, 13–43. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22216-5_2.
Texto completoBurguillo, Juan C. "Complex Networks". En Self-organizing Coalitions for Managing Complexity, 35–56. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69898-4_3.
Texto completoLiu, Jing, Hussein A. Abbass y Kay Chen Tan. "Complex Networks". En Evolutionary Computation and Complex Networks, 23–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-60000-0_2.
Texto completoErciyes, K. "Complex Networks". En Texts in Computer Science, 417–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73235-0_14.
Texto completoBertin, Eric. "Complex Networks". En Statistical Physics of Complex Systems, 181–206. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79949-6_6.
Texto completoAmaral, Ines. "Complex Networks". En Encyclopedia of Big Data, 198–201. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_40.
Texto completoAmaral, Ines. "Complex Networks". En Encyclopedia of Big Data, 1–3. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-32001-4_40-1.
Texto completoDanziger, Michael M., Louis M. Shekhtman, Amir Bashan, Yehiel Berezin y Shlomo Havlin. "Vulnerability of Interdependent Networks and Networks of Networks". En Understanding Complex Systems, 79–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23947-7_5.
Texto completoActas de conferencias sobre el tema "Complex Networks of treaties"
Yang, Zhun, Adam Ishay y Joohyung Lee. "NeurASP: Embracing Neural Networks into Answer Set Programming". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/243.
Texto completoXie, Chunyu, Ce Li, Baochang Zhang, Chen Chen, Jungong Han y Jianzhuang Liu. "Memory Attention Networks for Skeleton-based Action Recognition". En Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/227.
Texto completoHwang, Yunsuk, Jiajing Lin, David Schechter y Ding Zhu. "Predicting Well Performance in Naturally Fractured Reservoir". En ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-11604.
Texto completoNita, Andreea y Laurentiu Rozylowicz. "Dynamics of the international environmental treaties - perspectives for future cooperation". En 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2020. http://dx.doi.org/10.1109/asonam49781.2020.9381333.
Texto completoCai, Derun, Moxian Song, Chenxi Sun, Baofeng Zhang, Shenda Hong y Hongyan Li. "Hypergraph Structure Learning for Hypergraph Neural Networks". En Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/267.
Texto completoMa, Yuan-sheng, Xu Liu, Pei-fu Gu, Hai-feng Li y Jing-fa Tang. "Evaluation of Chracteristic of Optical Thin Film By Neural Network System". En Optical Interference Coatings. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/oic.1995.mb13.
Texto completoMcClure, Mark W., Mohsen Babazadeh, Sogo Shiozawa y Jian Huang. "Fully Coupled Hydromechanical Simulation of Hydraulic Fracturing in Three-Dimensional Discrete Fracture Networks". En SPE Hydraulic Fracturing Technology Conference. SPE, 2015. http://dx.doi.org/10.2118/spe-173354-ms.
Texto completoSchulz, Alexander, Fabian Hinder y Barbara Hammer. "DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality Reduction". En Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/319.
Texto completoRibeiro, Lionel H., Huina Li y Jason E. Bryant. "Use of a CO2-Hybrid Fracturing Design to Enhance Production from Unpropped Fracture Networks". En SPE Hydraulic Fracturing Technology Conference. SPE, 2015. http://dx.doi.org/10.2118/spe-173380-ms.
Texto completoTrajkovic, Ljiljana. "Complex Networks". En 2020 IEEE 19th International Conference on Cognitive Informatics & Cognitive Computing (ICCI*CC). IEEE, 2020. http://dx.doi.org/10.1109/iccicc50026.2020.9450254.
Texto completoInformes sobre el tema "Complex Networks of treaties"
Kleinberg, Robert D. Kleinberg Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2014. http://dx.doi.org/10.21236/ada612226.
Texto completoLai, Ying-Cheng. Security of Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, febrero de 2010. http://dx.doi.org/10.21236/ada567229.
Texto completoLai, Ying C. Predicting and Controlling Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, junio de 2015. http://dx.doi.org/10.21236/ada619238.
Texto completoAlbert-Laszlo Barabasi. The Organization of Complex Metabolic Networks. Office of Scientific and Technical Information (OSTI), mayo de 2006. http://dx.doi.org/10.2172/881797.
Texto completoE, Weinan, Jian Liu, Eric Vanden-Eijnden, A. Nadar, Tiejun Li, Hao Shen, D. Crommelin y Jianfeng Lu. Structure and Dynamics of Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, junio de 2012. http://dx.doi.org/10.21236/ada574664.
Texto completoColbaugh, Richard, Kristin Glass y Gerald Willard. Analysis of complex networks using aggressive abstraction. Office of Scientific and Technical Information (OSTI), octubre de 2008. http://dx.doi.org/10.2172/1145172.
Texto completoMilenkovic, Olgica y Angelia Nedich. Compressive Sensing and Coding for Complex Networks. Fort Belvoir, VA: Defense Technical Information Center, marzo de 2013. http://dx.doi.org/10.21236/ada582392.
Texto completoKnio, Omar M. Analysis and Reduction of Complex Networks Under Uncertainty. Office of Scientific and Technical Information (OSTI), abril de 2014. http://dx.doi.org/10.2172/1129444.
Texto completoGhanem, Roger G. Analysis and Reduction of Complex Networks Under Uncertainty. Office of Scientific and Technical Information (OSTI), julio de 2014. http://dx.doi.org/10.2172/1148680.
Texto completoDorfler, Florian, Michael Chertkov y Francesco Bullo. Synchronization in Complex Oscillator Networks and Smart Grids. Office of Scientific and Technical Information (OSTI), julio de 2012. http://dx.doi.org/10.2172/1047105.
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