Gotowa bibliografia na temat „Complex conductance networks”
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Artykuły w czasopismach na temat "Complex conductance networks"
Xiong, Kezhao, Zonghua Liu, Chunhua Zeng i Baowen Li. "Thermal-siphon phenomenon and thermal/electric conduction in complex networks". National Science Review 7, nr 2 (2.09.2019): 270–77. http://dx.doi.org/10.1093/nsr/nwz128.
Pełny tekst źródłaLópez, Eduardo, Shai Carmi, Shlomo Havlin, Sergey V. Buldyrev i H. Eugene Stanley. "Anomalous electrical and frictionless flow conductance in complex networks". Physica D: Nonlinear Phenomena 224, nr 1-2 (grudzień 2006): 69–76. http://dx.doi.org/10.1016/j.physd.2006.09.031.
Pełny tekst źródłaNykamp, Duane Q., i Daniel Tranchina. "A Population Density Approach That Facilitates Large-Scale Modeling of Neural Networks: Extension to Slow Inhibitory Synapses". Neural Computation 13, nr 3 (1.03.2001): 511–46. http://dx.doi.org/10.1162/089976601300014448.
Pełny tekst źródłaNarantsatsralt, Ulzii-Utas, i Sanggil Kang. "Social Network Community Detection Using Agglomerative Spectral Clustering". Complexity 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3719428.
Pełny tekst źródłaLiao, Zhifang, Lite Gu, Xiaoping Fan, Yan Zhang i Chuanqi Tang. "Detecting the Structural Hole for Social Communities Based on Conductance–Degree". Applied Sciences 10, nr 13 (29.06.2020): 4525. http://dx.doi.org/10.3390/app10134525.
Pełny tekst źródłaLi, Xujun, Yezheng Liu, Yuanchun Jiang i Xiao Liu. "Identifying social influence in complex networks: A novel conductance eigenvector centrality model". Neurocomputing 210 (październik 2016): 141–54. http://dx.doi.org/10.1016/j.neucom.2015.11.123.
Pełny tekst źródłaCase, Daniel J., Jean-Régis Angilella i Adilson E. Motter. "Spontaneous oscillations and negative-conductance transitions in microfluidic networks". Science Advances 6, nr 20 (maj 2020): eaay6761. http://dx.doi.org/10.1126/sciadv.aay6761.
Pełny tekst źródłaCARTLING, BO. "A LOW-DIMENSIONAL, TIME-RESOLVED AND ADAPTING MODEL NEURON". International Journal of Neural Systems 07, nr 03 (lipiec 1996): 237–46. http://dx.doi.org/10.1142/s012906579600021x.
Pełny tekst źródładi Volo, Matteo, Alberto Romagnoni, Cristiano Capone i Alain Destexhe. "Biologically Realistic Mean-Field Models of Conductance-Based Networks of Spiking Neurons with Adaptation". Neural Computation 31, nr 4 (kwiecień 2019): 653–80. http://dx.doi.org/10.1162/neco_a_01173.
Pełny tekst źródłaRote, Günter. "Characterization of the Response Maps of Alternating-Current Networks". Electronic Journal of Linear Algebra 36, nr 36 (14.10.2020): 698–703. http://dx.doi.org/10.13001/ela.2020.4981.
Pełny tekst źródłaRozprawy doktorskie na temat "Complex conductance networks"
Havlin, Shlomo, Eduardo López, Sergey V. Buldyrev i H. Eugene Stanley. "Anomalous conductance and diffusion in complex networks". Universitätsbibliothek Leipzig, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-195170.
Pełny tekst źródłaHavlin, Shlomo, Eduardo López, Sergey V. Buldyrev i H. Eugene Stanley. "Anomalous conductance and diffusion in complex networks". Diffusion fundamentals 2 (2005) 4, S. 1-11, 2005. https://ul.qucosa.de/id/qucosa%3A14337.
Pełny tekst źródłaYoussef, Mina Nabil. "Measure of robustness for complex networks". Diss., Kansas State University, 2012. http://hdl.handle.net/2097/13689.
Pełny tekst źródłaDepartment of Electrical and Computer Engineering
Caterina Scoglio
Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance ($VC_{SIS}$) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible ($SIS$) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, $VC_{SIS}$ provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barab\'si-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric $VC_$ is introduced to assess the robustness of networks with respect to the spread of susceptible/infected/recovered ($SIR$) epidemics. To compute $VC_$, we propose a novel individual-based approach to model the spread of $SIR$ epidemics in networks, which captures the infection size for a given effective infection rate. Thus, $VC_$ quantitatively integrates the infection strength with the corresponding infection size. To optimize the $VC_$ metric, a new mitigation strategy is proposed, based on a temporary reduction of contacts in social networks. The social contact network is modeled as a weighted graph that describes the frequency of contacts among the individuals. Thus, we consider the spread of an epidemic as a dynamical system, and the total number of infection cases as the state of the system, while the weight reduction in the social network is the controller variable leading to slow/reduce the spread of epidemics. Using optimal control theory, the obtained solution represents an optimal adaptive weighted network defined over a finite time interval. Moreover, given the high complexity of the optimization problem, we propose two heuristics to find the near optimal solutions by reducing the contacts among the individuals in a decentralized way. Finally, the cascading failures that can take place in power grids and have recently caused several blackouts are studied. We propose a new metric to assess the robustness of the power grid with respect to the cascading failures. The power grid topology is modeled as a network, which consists of nodes and links representing power substations and transmission lines, respectively. We also propose an optimal islanding strategy to protect the power grid when a cascading failure event takes place in the grid. The robustness metrics are numerically evaluated using real and synthetic networks to quantify their robustness with respect to disturbing dynamics. We show that the proposed metrics outperform the classical metrics in quantifying the robustness of networks and the efficiency of the mitigation strategies. In summary, our work advances the network science field in assessing the robustness of complex networks with respect to various disturbing dynamics.
Young, Stephen J. "Random dot product graphs a flexible model for complex networks". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26548.
Pełny tekst źródłaCommittee Chair: Mihail, Milena; Committee Member: Lu, Linyuan; Committee Member: Sokol, Joel; Committee Member: Tetali, Prasad; Committee Member: Trotter, Tom; Committee Member: Yu, Xingxing. Part of the SMARTech Electronic Thesis and Dissertation Collection.
Mendieta, Tenorio Aída. "Clay characterization using spectral induced polarization". Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS050.
Pełny tekst źródłaClays are ubiquitously present in the Earth’s near surface and they have a high impact on the permeability of a system. Due to this property, clay formations are used in a variety of geology related applications (oil and gas, geothermal, nuclear waste storage, critical zone research, among others). Clays have a high surface charge and a high specific surface area, this property gives clays a particularly strong electrical double layer (EDL). Spectral induced polarization (SIP) is an active geo-electrical method thatmeasures in a non-invasive manner the frequency-dependent complex conductivity of a geo-material from themHz to the kHz. The complex conductivity informs about the ability the probed material has to conduct an electrical current and the ability to polarize (to reversibly store electrical charges). This thesis presents a detailed laboratory protocol to obtain SIP measurements of different types of clay at varying salinities, as well as an artificial heterogeneous mixture of illite and red montmorillonite with a salinity of around 10¡2 mol L¡1. The results of the first study show that the real part of the electrical conductivity increases with salinity, but the imaginary part increases until a maxima and then decreases. An interpretation of the decrease can come fromthe fact that clays coagulate at high salinities. The potential coagulation of clays would alter the pore space and then alter the polarization mechanisms in play. Furthermore, when comparing the ratio of the surface conductivity (imaginary versus real) of these resultswith other data in the literature, we notice that this ratio decreaseswith clay content. For the second study, we observe that red montmorillonite dominates the polarization with respect to illite. However, both clays effect the conduction of the mixtures. Mixing laws are an effective approach to model the complex conductivity of these heterogeneous mixtures. Complex conductance network models are better at predicting the shape of the polarization spectra. The results of this thesis work open new opportunities for clay characterization using SIP
Części książek na temat "Complex conductance networks"
Koch, Christof. "Simplified Models of Individual Neurons". W Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.003.0020.
Pełny tekst źródłaStreszczenia konferencji na temat "Complex conductance networks"
Emerson, David R., i Robert W. Barber. "Designing Efficient Microvascular Networks Using Conventional Microfabrication Techniques". W ASME 2009 Second International Conference on Micro/Nanoscale Heat and Mass Transfer. ASMEDC, 2009. http://dx.doi.org/10.1115/mnhmt2009-18312.
Pełny tekst źródłaCreasy, M. Austin, i Donald J. Leo. "Modeling Bilayer Systems as Electrical Networks". W ASME 2010 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2010. http://dx.doi.org/10.1115/smasis2010-3791.
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