Thematische Bibliographien / Graph dynamics

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Auswahl der wissenschaftlichen Literatur zum Thema „Graph dynamics“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 25. Juli 2025

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Zeitschriftenartikel zum Thema "Graph dynamics"

Huang, Xueqin, Xianqiang Zhu, Xiang Xu, Qianzhen Zhang, and Ailin Liang. "Parallel Learning of Dynamics in Complex Systems." Systems 10, no. 6 (2022): 259. http://dx.doi.org/10.3390/systems10060259.

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Dynamics always exist in complex systems. Graphs (complex networks) are a mathematical form for describing a complex system abstractly. Dynamics can be learned efficiently from the structure and dynamics state of a graph. Learning the dynamics in graphs plays an important role in predicting and controlling complex systems. Most of the methods for learning dynamics in graphs run slowly in large graphs. The complexity of the large graph’s structure and its nonlinear dynamics aggravate this problem. To overcome these difficulties, we propose a general framework with two novel methods in this pape

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Li, Jintang, Zhouxin Yu, Zulun Zhu, et al. "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8588–96. http://dx.doi.org/10.1609/aaai.v37i7.26034.

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Recent years have seen a surge in research on dynamic graph representation learning, which aims to model temporal graphs that are dynamic and evolving constantly over time. However, current work typically models graph dynamics with recurrent neural networks (RNNs), making them suffer seriously from computation and memory overheads on large temporal graphs. So far, scalability of dynamic graph representation learning on large temporal graphs remains one of the major challenges. In this paper, we present a scalable framework, namely SpikeNet, to efficiently capture the temporal and structural pa

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Ahmed, Mouhamadou WADE. "Tight bounds on exploration of constantly connected cacti-paths." World Journal of Advanced Research and Reviews 12, no. 1 (2021): 355–61. https://doi.org/10.5281/zenodo.5594859.

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In this paper, we study the necessary and sufficient time to explore constantly connected dynamics graphs by a mobile entity (agent). A dynamic graph is constantly connected if for each time units, there exists a stable connected spanning tree [10]. We focus on the case where the underlying graph is a cactus-path (graph reduced to a path of  rings in which two neighbor rings have at most one vertex in common) and we assume that the agent knows the dynamics of the graph. We show that time units are necessary and sufficient to explore any constantly connected dynamic graph based on the cact

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Ahmed Mouhamadou WADE. "Tight bounds on exploration of constantly connected cacti-paths." World Journal of Advanced Research and Reviews 12, no. 1 (2021): 355–61. http://dx.doi.org/10.30574/wjarr.2021.12.1.0534.

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In this paper, we study the necessary and sufficient time to explore constantly connected dynamics graphs by a mobile entity (agent). A dynamic graph is constantly connected if for each time units, there exists a stable connected spanning tree [10]. We focus on the case where the underlying graph is a cactus-path (graph reduced to a path of k rings in which two neighbor rings have at most one vertex in common) and we assume that the agent knows the dynamics of the graph. We show that 5n - Θ(1) time units are necessary and sufficient to explore any constantly connected dynamic graph based on th

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Mouhamadou Wade, Ahmed. "EXPLORATION WITH RETURN OF HIGHLY DYNAMIC NETWORKS." International Journal of Advanced Research 9, no. 10 (2021): 315–19. http://dx.doi.org/10.21474/ijar01/13550.

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In this paper, we study the necessary and sufficient time to explore with return constantly connected dynamic networks modelled by a dynamic graphs. Exploration with return consists, for an agent operating in a dynamic graph, of visiting all the vertices of the graph and returning to the starting vertex. We show that for constantly connected dynamic graphs based on a ring of sizen,3n-4 time units are necessary and sufficient to explore it. Assuming that the agent knows the dynamics of the graph.

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Di Ianni, Miriam. "Game of Life-like Opinion Dynamics: Generalizing the Underpopulation Rule." AppliedMath 3, no. 1 (2022): 10–36. http://dx.doi.org/10.3390/appliedmath3010002.

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Graph dynamics for a node-labeled graph is a set of updating rules describing how the labels of each node in the graph change in time as a function of the global set of labels. The underpopulation rule is graph dynamics derived by simplifying the set of rules constituting the Game of Life. It is known that the number of label configurations met by a graph during the dynamic process defined by such rule is bounded by a polynomial in the size of the graph if the graph is undirected. As a consequence, predicting the labels evolution is an easy problem (i.e., a problem in P) in such a case. In thi

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Zhang, Lei, Zhiqian Chen, Chang-Tien Lu, and Liang Zhao. "From “Dynamics on Graphs” to “Dynamics of Graphs”: An Adaptive Echo-State Network Solution (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13111–12. http://dx.doi.org/10.1609/aaai.v36i11.21692.

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Many real-world networks evolve over time, which results in dynamic graphs such as human mobility networks and brain networks. Usually, the “dynamics on graphs” (e.g., node attribute values evolving) are observable, and may be related to and indicative of the underlying “dynamics of graphs” (e.g., evolving of the graph topology). Traditional RNN-based methods are not adaptive or scalable for learn- ing the unknown mappings between two types of dynamic graph data. This study presents a AD-ESN, and adaptive echo state network that can automatically learn the best neural net- work architecture fo

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Chen, Haiyan, and Fuji Zhang. "Spectral Dynamics of Graph Sequences Generated by Subdivision and Triangle Extension." Electronic Journal of Linear Algebra 32 (February 6, 2017): 454–63. http://dx.doi.org/10.13001/1081-3810.3583.

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For a graph G and a unary graph operation X, there is a graph sequence \G_k generated by G_0=G and G_{k+1}=X(G_k). Let Sp({G_k}) denote the set of normalized Laplacian eigenvalues of G_k. The set of limit points of \bigcup_{k=0}^\infty Sp(G_k)$, $\liminf_{k\rightarrow\infty}Sp(G_k) and $\limsup_{k\rightarrow \infty}Sp(G_k)$ are considered in this paper for graph sequences generated by two operations: subdivision and triangle extension. It is obtained that the spectral dynamic of graph sequence generated by subdivision is determined by a quadratic function, which is closely related to the the w

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Fahrenthold, E. P., and J. D. Wargo. "Lagrangian Bond Graphs for Solid Continuum Dynamics Modeling." Journal of Dynamic Systems, Measurement, and Control 116, no. 2 (1994): 178–92. http://dx.doi.org/10.1115/1.2899209.

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The limitations of existing continuum bond graph modeling techniques have effectively precluded their use in large order problems, where nonrepetitive graph structures and causal patterns are normally present. As a result, despite extensive publication of bond graph models for continuous systems simulations, bond graph methods have not offered a viable alternative to finite element analysis for the vast majority of practical problems. However, a new modeling approach combining Lagrangian (mass fixed) bond graphs with a selected finite element discretization scheme allows for direct simulation

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Chen, Lanlan, Kai Wu, Jian Lou, and Jing Liu. "Signed Graph Neural Ordinary Differential Equation for Modeling Continuous-Time Dynamics." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 8 (2024): 8292–301. http://dx.doi.org/10.1609/aaai.v38i8.28670.

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Modeling continuous-time dynamics constitutes a foundational challenge, and uncovering inter-component correlations within complex systems holds promise for enhancing the efficacy of dynamic modeling. The prevailing approach of integrating graph neural networks with ordinary differential equations has demonstrated promising performance. However, they disregard the crucial signed information potential on graphs, impeding their capacity to accurately capture real-world phenomena and leading to subpar outcomes. In response, we introduce a novel approach: a signed graph neural ordinary differentia

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Dissertationen zum Thema "Graph dynamics"

Ribeiro, Andre Figueiredo. "Graph dynamics : learning and representation." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/34184.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2006. Includes bibliographical references (p. 58-60). Graphs are often used in artificial intelligence as means for symbolic knowledge representation. A graph is nothing more than a collection of symbols connected to each other in some fashion. For example, in computer vision a graph with five nodes and some edges can represent a table - where nodes correspond to particular shape descriptors for legs and a top, and edges to particular spatial relations. As a fram

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Kuhlman, Christopher James. "Generalizations of Threshold Graph Dynamical Systems." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/76765.

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Dynamics of social processes in populations, such as the spread of emotions, influence, language, mass movements, and warfare (often referred to individually and collectively as contagions), are increasingly studied because of their social, political, and economic impacts. Discrete dynamical systems (discrete in time and discrete in agent states) are often used to quantify contagion propagation in populations that are cast as graphs, where vertices represent agents and edges represent agent interactions. We refer to such formulations as graph dynamical systems. For social applications, thresho

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Arnlind, Joakim. "Graph Techniques for Matrix Equations and Eigenvalue Dynamics." Doctoral thesis, KTH, Matematik (Inst.), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4608.

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One way to construct noncommutative analogues of a Riemannian manifold Σ is to make use of the Toeplitz quantization procedure. In Paper III and IV, we construct C-algebras for a continuously deformable class of spheres and tori, and by introducing the directed graph of a representation, we can completely characterize the representation theory of these algebras in terms of the corresponding graphs. It turns out that the irreducible representations are indexed by the periodic orbits and N-strings of an iterated map s:(reals) 2→(reals)2 associated to the algebra. As our construction allows for t

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Ayazifar, Babak 1967. "Graph spectra and modal dynamics of oscillatory networks." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/16913.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2003. Includes bibliographical references (leaves 186-191). This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Our research focuses on developing design-oriented analytical tools that enable us to better understand how a network comprising dynamic and static elements behaves when it is set in oscillatory motion, and how the interconnection topology relates to the spectral p

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Homer, Martin Edward. "Bifurcations and dynamics of piecewise smooth dynamical systems of arbitrary dimension." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299271.

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Lee, Daryl Hsu Ann. "Toward large-graph comparison measures to understand Internet topology dynamics." Thesis, Monterey, California: Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37658.

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Approved for public release; distribution is unlimited By measuring network changes, we can get a better understanding of a network. Extending this to the Internet, we are able to understand the constantly occuring changes on an international scale. In this research, we propose a measure that conveys the relative magnitude of the change between two networks (i.e., Internet topology). The measure is normalised and intuitively gives an indication of whether the change is small or large. We start off by applying this measure to standard common graphs, as well as random graphs. These graphs we

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Giscard, Pierre-Louis. "A graph theoretic approach to matrix functions and quantum dynamics." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ceef15b0-eed2-4615-a9f2-f9efbef470c9.

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Many problems in applied mathematics and physics are formulated most naturally in terms of matrices, and can be solved by computing functions of these matrices. For example, in quantum mechanics, the coherent dynamics of physical systems is described by the matrix exponential of their Hamiltonian. In state of the art experiments, one can now observe such unitary evolution of many-body systems, which is of fundamental interest in the study of many-body quantum phenomena. On the other hand the theoretical simulation of such non-equilibrium many-body dynamics is very challenging. In this thesis,

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Ayala-Hoffmann, Jose. "Global behavior of graph dynamics with applications to Markov chains." [Ames, Iowa : Iowa State University], 2008.

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Magkakis, Andreas Gkompel. "Counting, modular counting and graph homomorphisms." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:42be90cd-75b5-43ec-ad2e-5d513420bdc0.

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A homomorphism from a graph G to a graph H is a function from V (G) to V (H) that preserves edges. Many combinatorial structures that arise in mathematics and in computer science can be represented naturally as graph homomorphisms and as weighted sums of graph homomorphisms. In this thesis we study the complexity of various problems related to graph homomorphisms.

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Budai, Daniel, and David Jallo. "The Market Graph : A study of its characteristics, structure & dynamics." Thesis, KTH, Matematisk statistik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-103094.

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In this thesis we have considered three different market graphs; one solely based on stock returns, another one based on stock returns with vertices weighted with a liquidity measure and lastly one based on correlations of volume fluctuations. Research is conducted on two different markets; the Swedish and the American stock market. We want to introduce graph theory as a method for representing the stock market in order to show that one can more fully understand the structural properties and dynamics of the stock market by studying the market graph. We found many signs of increased globalizati

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Bücher zum Thema "Graph dynamics"

Prisner, E. Graph dynamics. Longman, 1995.

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Prisner, E. Graph dynamics. Longman, 1995.

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Świder, Jerzy. Macierzowe grafy hybrydowe w opisie drgających, złożonych układów mechanicznych. Wydawn. Politechniki Śląskiej, 1991.

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Brown, Forbes T. Engineering system dynamics: A unified graph-centered approach. 2nd ed. CRC/Taylor & Francis, 2006.

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Horst, Bunke, ed. A graph-theoretic approach to enterprise network dynamics. Birkhäuser, 2007.

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Mariusz, Urbaʹnski, ed. Graph directed Markov systems: Geometry and dynamics of limit sets. Cambridge University Press, 2003.

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Watts, Duncan J. Small worlds: The dynamics of networks between order and randomness. Princeton University Press, 1999.

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Krishnamoorthy, Raju. Dynamics, Graph Theory, and Barsotti-Tate Groups: Variations on a Theme of Mochizuki. [publisher not identified], 2016.

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Osipenko, Georgiy. Computer-oriented methods of dynamic systems. INFRA-M Academic Publishing LLC., 2023. http://dx.doi.org/10.12737/1912470.

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The methods of studying the global properties of dynamic systems based on the construction of a symbolic image of this system are considered. A symbolic image is an oriented graph, which is an approximation to a dynamical system and is constructed by discretizing the phase space. The symbolic dynamics generated by the oriented graph reflects the dynamics of the system under study. The symbolic image is a tool of theoretical research and the basis of computer-oriented methods for the numerical study of nonlocal properties of dynamical systems. Meets the requirements of the federal state e

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Jaume, Llibre, and Misiurewicz Michał 1948-, eds. Combinatorial dynamics and entropy in dimension one. World Scientific, 1993.

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Buchteile zum Thema "Graph dynamics"

Arrighi, Pablo, and Gilles Dowek. "Causal Graph Dynamics." In Automata, Languages, and Programming. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31585-5_9.

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Cittadini, Luca, Tiziana Refice, Alessio Campisano, Giuseppe Di Battista, and Claudio Sasso. "Policy-Aware Visualization of Internet Dynamics." In Graph Drawing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00219-9_43.

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Maignan, Luidnel, and Antoine Spicher. "Causal Graph Dynamics and Kan Extensions." In Graph Transformation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64285-2_5.

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Gad, Osama. "Bond Graph Modeling Technique." In System Dynamics. CRC Press, 2024. http://dx.doi.org/10.1201/9781032685656-3.

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Jain, Abhinandan. "Graph Theory Connections." In Robot and Multibody Dynamics. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-7267-5_8.

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Fagnani, Fabio, and Paolo Frasca. "Graph Theory." In Introduction to Averaging Dynamics over Networks. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68022-4_1.

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King, R. B. "Polyhedral Dynamics." In Graph Theoretical Approaches to Chemical Reactivity. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-1202-4_4.

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Arrighi, Pablo, Simon Martiel, and Simon Perdrix. "Reversible Causal Graph Dynamics." In Reversible Computation. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40578-0_5.

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Dahal, Pragyan, Stefano Arrigoni, Mario Bijelic, and Francesco Braghin. "Vehicle State Estimation Through Dynamics Modeled Factor Graph." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70392-8_119.

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AbstractEgo Vehicle state estimation is integral to every autonomous driving software stack. Thereby, the estimation of the state and its components as for example the side slip angle, is a crucial component to track the vehicle maneuvers. In the absence of a direct sensor measuring side slip angle, most of the existing literature either use observers like Kalman Filters or non-modular factor graphs by modeling lateral dynamics. However, the modularity of such graphs, to integrate multiple asynchronous sensors that provide disentangled measurements, like LiDAR, GNSS, and IMU is still overlooke

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van Benthem, Johan, and Fenrong Liu. "Graph Games and Logic Design." In Knowledge, Proof and Dynamics. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2221-5_7.

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Konferenzberichte zum Thema "Graph dynamics"

Zakharov, Alexei. "Machine Part Recognition based on Transfer Learning and Knowledge Graph." In 2024 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2024. https://doi.org/10.1109/dynamics64718.2024.10838668.

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Zakharov, Alexei. "Object Detection Based on Graph Neural Networks and Few-Shot Learning for Industrial Safety Control." In 2024 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2024. https://doi.org/10.1109/dynamics64718.2024.10838689.

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Kim, J. Taery, Archit Naik, Isuru Jayarathne, Sehoon Ha, and Jouh Yeong Chew. "Modeling social interaction dynamics using temporal graph networks." In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE, 2024. http://dx.doi.org/10.1109/ro-man60168.2024.10731450.

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Deng, Yuhong, Kai Mo, Chongkun Xia, and Xueqian Wang. "Learning Language-Conditioned Deformable Object Manipulation with Graph Dynamics." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610890.

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Yang, Sheng, Daixi Jia, Lipeng Chen, Kunyu Li, Fengge Wu, and JunSuo Zhao. "GOAT: Learning Multi-Body Dynamics Using Graph Neural Network with Restrains." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831396.

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Offord, Edward, Judith Lutton, and Till Bretschneider. "Heterogeneous Graph Neural Networks for Analysing Spatio-Temporal Cell Surface Dynamics." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI). IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635109.

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Jia, Renjun, Kaiming Yang, Dawei Cheng, Li Han, and Yuqi Liang. "Graph-Driven Insights: Enhancing Stock Market Prediction with Relational Temporal Dynamics." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889271.

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Zhang, Yuan, Ran Zhang, and Huifeng Li. "Dynamics-Constrained Graph Learning Approach for Hypersonic Vehicle Path Planning Problem." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886294.

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Hagberg, Aric A., Daniel A. Schult, and Pieter J. Swart. "Exploring Network Structure, Dynamics, and Function using NetworkX." In Python in Science Conference. SciPy, 2008. http://dx.doi.org/10.25080/tcwv9851.

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NetworkX is a Python language package for exploration and analysis of networks and network algorithms. The core package provides data structures for representing many types of networks, or graphs, including simple graphs, directed graphs, and graphs with parallel edges and self-loops. The nodes in NetworkX graphs can be any (hashable) Python object and edges can contain arbitrary data; this flexibility makes NetworkX ideal for representing networks found in many different scientific fields. In addition to the basic data structures many graph algorithms are implemented for calculating network p

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Belim, Sergey V., and Anton N. Mironenko. "Using the graph-theoretic approach to solving the Role Mining problem." In 2018 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2018. http://dx.doi.org/10.1109/dynamics.2018.8601487.

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Berichte der Organisationen zum Thema "Graph dynamics"

Djidjev, Hristo Nikolov, Georg Hahn, Susan M. Mniszewski, Christian Francisco Negre, Anders Mauritz Niklasson, and Vivek Sardeshmukh. Graph Partitioning Methods for Fast Parallel Quantum Molecular Dynamics. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1330079.

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Thulasidasan, Sunil. The Graph Laplacian and the Dynamics of Complex Networks. Office of Scientific and Technical Information (OSTI), 2012. http://dx.doi.org/10.2172/1043504.

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Chew, Geoffrey F. Quantum dynamics via Planck-scale-stepped action-carrying 'Graph Paths'. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/813522.

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Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration

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Soloviev, Vladimir, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk, and Lukáš Pichl. Complex networks theory and precursors of financial crashes. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4119.

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Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex networ

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Mesbahi, Mehran. Dynamic Security and Robustness of Networked Systems: Random Graphs, Algebraic Graph Theory, and Control over Networks. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada567125.

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Gallagher, B., and T. Eliassi-Rad. API Requirements for Dynamic Graph Prediction. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/1036864.

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Khanna, S., R. Motwani, and R. H. Wilson. On certificates and lookahead in dynamic graph problems. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/93769.

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Chen, H., L. Jalil, and S. Dontula. Dynamic Flooding on Dense Graphs. Edited by T. Li and P. Psenak. RFC Editor, 2024. http://dx.doi.org/10.17487/rfc9667.

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Bhatele, Abhinav, Sebastien Fourestier, Harshitha Menon, Laxmikant V. Kale, and Francois Pellegrini. Applying graph partitioning methods in measurement-based dynamic load balancing. Office of Scientific and Technical Information (OSTI), 2011. http://dx.doi.org/10.2172/1114706.

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