Results 271 to 280 of about 223,064 (315)
Highly hydrophilic surfaces (water contact angle, ≈17.7°) exhibiting surprising water slipping performance (sliding angle, ≈7.3°) are successfully prepared via simple chemisorption of polyethylene glycol (PEG) organosilane and subsequent alkali‐treatment.
Hyeonjin Kim +5 more
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Sulfur‐doped graphitized carbon nanofibers act as adaptive catalyst–support platforms, enabling dynamic sulfur‐mediated reconstruction and strong metal–support interactions. This unique behavior enhances catalyst stability and controls reaction pathways, achieving highly selective urea oxidation (∼92% N2) coupled with efficient hydrogen evolution ...
Melanie Guillén‐Soler +4 more
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Coagulative granular hydrogels are composed of packed thrombin‐functionalized microgels that catalyze the conversion of fibrinogen into a secondary fibrin network, filling the interstitial voids. This bio‐inspired approach stabilizes the biomaterial to match the robustness of bulk hydrogels without compromising injectability, mimicking the initial ...
Zhipeng Deng +16 more
wiley +1 more source
The incorporation of nondigested ECM and synthetic polymers into a co‐electrospinning system enables the decoupling of bioactivity and mechanical properties within a single wrap. This technique is used to develop a multifunctional bone wrap that achieves augmented membrane durability, sustained infection control, and enhanced vascularity for use in ...
Sarah Jones +14 more
wiley +1 more source
Packed hydrogel microfiber (PHM) materials consist of flexible and high aspect ratio hydrogel components that, as a bulk material, are simultaneously mechanically robust and dynamic. Cells cultured in or on PHM scaffolds can be influenced by topographical cues or interact with a dynamic environment that permits cell spreading and multicellular ...
M. Gregory Grewal +7 more
wiley +1 more source
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Computational Statistics & Data Analysis, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carlos E. Rodríguez, Stephen G. Walker
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Carlos E. Rodríguez, Stephen G. Walker
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2020 Australian and New Zealand Control Conference (ANZCC), 2020
Particle filters are often explained by either heuristics arguments or complex mathematics. Present day particle filters rely on various methods such as importance sampling, resampling method and resampling strategy. Moreover, there are different derivations for discrete and continuous time dynamic models. In this paper we offer a new simple derivation
Torben Knudsen, John Leth
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Particle filters are often explained by either heuristics arguments or complex mathematics. Present day particle filters rely on various methods such as importance sampling, resampling method and resampling strategy. Moreover, there are different derivations for discrete and continuous time dynamic models. In this paper we offer a new simple derivation
Torben Knudsen, John Leth
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Journal of Parallel and Distributed Computing, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Olivier Brun +2 more
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Olivier Brun +2 more
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Particle flow for particle filtering
2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2016Particle flow algorithms have been developed as an alternative to particle filtering. In these algorithms, there is no importance sampling, and particles are migrated from the prior to the posterior via a "flow", described by differential equations. Aside from a few special cases, implementations involve multiple approximations, and their impact on the
Yunpeng Li 0001 +2 more
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Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563), 2002
Sequential Bayesian estimation for dynamic state space models involves recursive estimation of hidden states based on noisy observations. The update of filtering and predictive densities for nonlinear models with non-Gaussian noise using Monte Carlo particle filtering methods is considered.
Jayesh H. Kotecha, Petar M. Djuric
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Sequential Bayesian estimation for dynamic state space models involves recursive estimation of hidden states based on noisy observations. The update of filtering and predictive densities for nonlinear models with non-Gaussian noise using Monte Carlo particle filtering methods is considered.
Jayesh H. Kotecha, Petar M. Djuric
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