Results 91 to 100 of about 3,102,730 (292)
Rock typing and causality analysis in unconventional formation using Bayes nets
Petroleum ResearchUnconventional formations are characterized by high heterogeneity and anisotropy, making it difficult to interpret logging data, perform core analysis, and create accurate petrophysical models.
Evgeny Chekhonin +5 more
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Frontiers in Computational Neuroscience, 2014 This work presents a spatial-component (SC) based approach to aid the diagnosis of Alzheimer's disease (AD) using magnetic resonance images. In this approach, the whole brain image is subdivided in regions or spatial components, and a Bayesian network is
Ignacio eA. Illán +3 more
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Frontiers in Water, 2020 The rivers of KwaZulu-Natal, South Africa, are being impacted by various anthropogenic activities that threaten their sustainability. Our study demonstrated how Bayesian networks could be used to conduct an environmental risk assessment of ...
Olalekan A. Agboola +3 more
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International Journal of Approximate Reasoning, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Manxia Liu +3 more
openaire +9 more sources
Bioengineered Interfaces for Peripheral Nerve Sensory Restoration
Advanced Materials, EarlyView.Half of amputees abandon their prosthetics for lack of feeling. This review charts the full path from peripheral nerve injury to restored sensation, through surgical, regenerative, noninvasive, and implanted approaches, and shows how injury type and interface material properties determine which strategy can deliver naturalistic feedback, and why ...
Sydney Swedick +4 more
wiley +1 more source
Towards data-centric control of sensor networks through Bayesian dynamic linear modelling [PDF]
, 2015 Wireless sensor networks usually operate in dynamic, stochastic environments. While the behaviour of individual nodes is important, they are better seen as contributors to a larger mission, and managing the sensing quality and performance of these ...
Dobson, Simon Andrew +3 more
core +1 more source
Journal of the Brazilian Computer Society, 1997 Bayesian techniques have been developed over many years in a range of different fields, but have only recently been applied to the problem of learning in neural networks. As well as providing a consistent framework for statistical pattern recognition, the Bayesian approach offers a number of practical advantages including a solution to the problem of ...
openaire +4 more sources
Advanced Materials Interfaces, EarlyView.This work critically reviews MXenes as highly effective multifunctional nanomaterials for the adsorption of radio‐contaminants, demonstrating a remarkable adsorption capacity of up to 1376.75 mg/g and cyclic stability of 2–8 cycles, with complexation, electrostatic interactions, and the numerical strength of MXene active sites playing a key operational
Stephen Sunday Emmanuel +1 more
wiley +1 more source
Advanced Manufacturing of Composite‐Based Systems for Energy Applications
Advanced Materials Technologies, EarlyView.Advanced manufacturing enables the integration of polymers, ceramics, metal oxides, and composites into architected microstructures with tailored transport pathways. By coupling material selection, manufacturing strategy, and structural design, multifunctional energy systems can simultaneously improve electrochemical performance, thermal management ...
Sri Vaishnavi Thummalapalli +13 more
wiley +1 more source
Theory-independent limits on correlations from generalized Bayesian networks
New Journal of Physics, 2014 Bayesian networks provide a powerful tool for reasoning about probabilistic causation, used in many areas of science. They are, however, intrinsically classical. In particular, Bayesian networks naturally yield the Bell inequalities.
Joe Henson, Raymond Lal, Matthew F Pusey
doaj +1 more source