V
V. Tejedor
Researcher at Pierre-and-Marie-Curie University
Publications - 11
Citations - 1764
V. Tejedor is an academic researcher from Pierre-and-Marie-Curie University. The author has contributed to research in topics: Random walk & Anomalous diffusion. The author has an hindex of 10, co-authored 11 publications receiving 1605 citations. Previous affiliations of V. Tejedor include Technische Universität München.
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Journal ArticleDOI
In Vivo Anomalous Diffusion and Weak Ergodicity Breaking of Lipid Granules
Jae-Hyung Jeon,V. Tejedor,Stas Burov,Eli Barkai,Christine Selhuber-Unkel,Christine Selhuber-Unkel,Kirstine Berg-Sørensen,Lene B. Oddershede,Ralf Metzler,Ralf Metzler +9 more
TL;DR: It is demonstrated that at short times the granules perform subdiffusion according to the laws of continuous time random walk theory and the associated violation of ergodicity leads to a characteristic turnover between two scaling regimes of the time averaged mean squared displacement.
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First-passage times in complex scale-invariant media
TL;DR: The analytical approach provides a universal scaling dependence of the mean FPT on both the volume of the confining domain and the source–target distance, which is applicable to a broad range of stochastic processes characterized by length-scale-invariant properties.
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Probing microscopic origins of confined subdiffusion by first-passage observables
TL;DR: The results show that first-passage observables provide tools to unambiguously discriminate between the two possible microscopic scenarios of subdiffusion, and suggest experiments that could help in determining the origin of subDiffusion in complex media, such as living cells.
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Global mean first-passage times of random walks on complex networks
TL;DR: It is shown that this minimal scaling of the GMFPT with the network size is realized under the simple condition that the random walk is transient at the target site and independently of the small-world, scale-free, or fractal properties of the network.
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Optimizing persistent random searches.
TL;DR: The results show that the distribution of targets plays a crucial role in the random search problem, and it is found that persistent random walks with exponential distribution of excursion lengths can minimize the search time, and in that sense perform better than any Levy walk.