Results 151 to 160 of about 207,453 (329)
Food Prices and Inflation Expectations in New Zealand
Agribusiness, EarlyView.ABSTRACT Food prices are conspicuous, and spending on food constitutes a considerable share of household expenditure. In this study, we use partially identified Bayesian structural vector autoregression models to analyze the effects of food price shocks on core inflation and 1‐ and 5‐year inflation expectations in New Zealand.
Puneet Vatsa +2 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
AI in chemical engineering: From promise to practice
AIChE Journal, EarlyView.Abstract Artificial intelligence (AI) in chemical engineering has moved from promise to practice: physics‐aware (gray‐box) models are gaining traction, reinforcement learning complements model predictive control (MPC), and generative AI powers documentation, digitization, and safety workflows.
Jia Wei Chew +4 more
wiley +1 more source
Prior Performance and Risk-Taking of Mutual Fund Managers: A Dynamic Bayesian Network Approach
, 2009 Manuel Ammann, Michael Verhofen
openalex +1 more source
Protective Consumption Behavior Under Smog: Using a Data-driven Dynamic Bayesian Network [PDF]
, 2021 Yuan Yu, Bo Fan
openalex +1 more source
Computational study of permeability in cardboard coating layers
AIChE Journal, EarlyView.Abstract We develop a virtual material structure model based on a combination of tessellations and Gaussian random fields for a coating layer of paperboard used for packaging and designed to facilitate printing on the surface. To fit the model to tomographic image data acquired using combined focused ion beam and scanning electron microscopy (FIB‐SEM),
Sandra Barman +6 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour +5 more
wiley +1 more source
Non-homogeneous dynamic Bayesian networks with edge-wise sequentially coupled parameters. [PDF]
Bioinformatics, 2020 Shafiee Kamalabad M, Grzegorczyk M.
europepmc +1 more source