Results 51 to 60 of about 7,477 (225)
A New Uncertainty Measure in Belief Entropy Framework [PDF]
International Journal of Advanced Computer Science and Applications, 2018 Belief entropy, which represents the uncertainty measure between several pieces of evidence in the Dempster-Shafer framework, is attracting increasing interest in research. It has been used in many applications and is mainly based on the theory of evidence. To quantify uncertainty, several measures have been proposed in the literature.
Moise Digrais Mambe +3 more
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Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion
Entropy, 2022 Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of ...
Yongchuan Tang, Yong Chen, Deyun Zhou
doaj +1 more source
Diversity and complexity in neural organoids
FEBS Letters, EarlyView.Neural organoid research aims to expand genetic diversity on one side and increase tissue complexity on the other. Chimeroids integrate multiple donor genomes within single organoids. Self‐organising multi‐identity organoids, exogenous cell seeding, or enforced assembly of region‐specific organoids contribute to tissue complexity.
Ilaria Chiaradia, Madeline A. Lancaster
wiley +1 more source
From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source
Sensors, 2017 The multi-sensor data fusion technique plays a significant role in fault diagnosis and in a variety of such applications, and the Dempster–Shafer evidence theory is employed to improve the system performance; whereas, it may generate a counter-intuitive ...
Fuyuan Xiao
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IEEE Access, 2021 Multisensor data fusion is extensively used to merge data from heterogeneous sensors in a smart environment. However, sensors provide noisy and uncertain information which is a big challenge for researchers.
Ihsan Ullah, Joosang Youn, Youn-Hee Han
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Reconstructing enzyme evolution by protein engineering
FEBS Letters, EarlyView.Natural enzyme evolution can be retraced by protein engineering methods such as directed evolution, rational design, and ancestral sequence reconstruction. These approaches reveal how enzymes emerged from ligand‐binding scaffolds, developed varying substrate preferences, formed oligomeric complexes, adapted to environmental changes, and evolved novel ...
Lukas Drexler +2 more
wiley +1 more source
An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis
IEEE Access, 2019 A multisensor system is used to improve the ability of monitoring and tracking accuracy of an engineering system since only one sensor is not enough, whereupon, faulty diagnosis, as a typical information fusion problem which is under multisensor ...
Zhongyan Wang, Fuyuan Xiao
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Golgi enzymes are retrieved from the plasma membrane to the trans‐Golgi network
FEBS Letters, EarlyView.Golgi enzymes are traditionally considered resident proteins retained within the Golgi apparatus. Here, we demonstrate that a subset transiently reaches the cell surface and is subsequently retrieved to the trans‐Golgi network via retrograde transport. Using a nanobody‐based toolkit, we uncover a dynamic trafficking cycle of several Golgi enzymes.
Dominik P. Buser, Tina Junne
wiley +1 more source
Entropy and MDL discretization of continuous variables for Bayesian belief networks [PDF]
International Journal of Intelligent Systems, 2000 An efficient algorithm for partitioning the range of a continuous variable to a discrete number of intervals, for use in the construction of Bayesian belief networks (BBNs), is presented here. The partitioning minimizes the information loss, relative to the number of intervals used to represent the variable.
Ellis J. Clarke, Bruce A. Barton
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