Introduction to Flexible Hierarchical

What is/are Flexible Hierarchical?

Flexible Hierarchical - In this work, flexible hierarchically porous carbon polyhedra embedded carbon nanofibers doped with N and S (NSCPCNF) were synthesized by electrospinning a metal–organic framework ZIF-67 and a thiourea incorporated polyacrylonitrile precursor with subsequent carbonization. ^[1]

flexible hierarchical structure

Individual linguistic axiological spheres are examined in terms of flexible hierarchical structures, the dynamism of which is determined by the characters’ life experience. ^[1] The polarity, roughness and wettability of CFs surface as well as the thickness of intermediate layer in composite have been significantly increased after rigid-flexible hierarchical structure was constructed. ^[2] The development of CM models on the basis of the von Mises distribution has allowed to integrate flexible hierarchical structures accounting for different sources of variation and affecting both mean and dispersion terms. ^[3] Thai public sector always described as a highly centralized system, with an inflexible hierarchical structure and high levels of formal relationships channeling its communications through public enterprises and institutions. ^[4]

flexible hierarchical bayesian

We estimate the effectiveness of 17 NPIs in Europe’s second wave from subnational case and death data by introducing a flexible hierarchical Bayesian transmission model and collecting the largest dataset of NPI implementation dates across Europe. ^[1] To take advantage of data collected at monitoring stations and the effect of secondary information such as meteorological factors, this paper proposes a flexible hierarchical Bayesian model (HBM) which can predict air pollutant concentrations in space and time. ^[2] We systematically revisited stroke outcome predictions by casting the data in a less confounded form and employing more integrative and flexible hierarchical Bayesian models. ^[3]

flexible hierarchical model

Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dynamics, as well as the variability between experimental units and, optionally, account for measurement error. ^[1] SDEMEMs are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dynamics, as well as the variability between experimental units and, optionally, account for measurement error. ^[2]