Results 151 to 160 of about 39,041 (304)
Applying Bayesian networks to model uncertainty in project scheduling
, 2009 PhDRisk Management has become an important part of Project Management. In spite of numerous advances in the field of Project Risk Management (PRM), handling uncertainty in complex projects still remains a challenge.
Khodakarami, Vahid
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Advanced Science, EarlyView.We present a chromosome‐level genome assembly of Siraitia grosvenorii and, through comparative genomics, uncover a conserved UGT73 tandem array driving triterpenoid saponin diversification in Cucurbitaceae. Crystalized SgUGT73AM30 further reveals the regioselectivity mechanism underlying its catalytic activity.
Guangyi Wang +13 more
wiley +1 more source
Gaussian copula-based Bayesian Networks for dynamic loads in mooring systems
Applied Ocean ResearchOffshore floating structures are experiencing harsh environmental conditions risking their safety. Therefore, mooring lines are crucial for ensuring structures’ stability.
R. Santjer +3 more
doaj +1 more source
Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics
Advanced Science, EarlyView.Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai +3 more
wiley +1 more source
Advanced Science, EarlyView.CHCHD10 loss in Alzheimer's disease is associated with mitochondrial dysfunction, epigenomic disruption, and tau pathology. Restoration of CHCHD10 shifts DNA methylation toward a non‐disease state and reduces tau and amyloid pathology, with KATNAL2 acting as a downstream effector.
Teresa M. Thomas +13 more
wiley +1 more source
Feature Dynamic Bayesian Networks
Proceedings of the 2nd Conference on Artificial General Intelligence (2009), 2009 Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I extend PhiMDP to PhiDBN.
openaire +3 more sources
Advanced Science, EarlyView.Correcting the apparent priming effect resolves systematic biases in Asian rice fertilizer nitrogen accounting. Net soil retention drops below 7%, while 48% of fertilizer escapes, inflicting US$98.53 billion in annual reactive‐nitrogen damages. High‐resolution mapping uncovers N‐risk archetypes across 42% of the rice area, delivering a spatially ...
Xiuyun Liu +5 more
wiley +1 more source
Link Quality Prediction for WSNs Based on Dynamic Bayesian Networks
工程科学与技术, 2017 :In wireless sensor networks,the link quality prediction is a basic issue in guarantying reliable data transmission and upper network protocol performance.In this paper,a link quality prediction mechanism based on dynamic Bayesian networks (DBN) was ...
SHU Jian +3 more
doaj
Social Sciences and Humanities OpenDecision-making in highly complex environments, such as environmental investment planning, often involves uncertainty, competing priorities, and the integration of both technical and human factors. From a psychological perspective, this raises challenges
Hasan Dinçer +3 more
doaj +1 more source
Bayesian neural network learning for repeat purchase modelling in direct marketing. [PDF]
We focus on purchase incidence modelling for a European direct mail company. Response models based on statistical and neural network techniques are contrasted.
Van den Poel, D +4 more
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