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
A hybrid approach combining Bayesian networks and logistic regression for enhancing risk assessment. [PDF]
Sci RepWei X, Dong Y.
europepmc +1 more source
Protection Motivation Theory and Farmers' Participation in Futures Markets: Evidence From Germany
Agribusiness, EarlyView.ABSTRACT This study examines why German farmers show limited adoption of commodity futures contracts despite substantial price volatility, applying Protection Motivation Theory (PMT) to understand the cognitive processes driving participation decisions in futures markets. Survey data from 303 German farmers collected in 2024 were analyzed using Partial
Hendrik Wever +2 more
wiley +1 more source
A guide to bayesian networks software for structure and parameter learning, with a focus on causal discovery tools. [PDF]
Front Syst BiolCanonaco F +4 more
europepmc +1 more source
Physics‐encoded transfer learning for scale‐up modeling of CHO cell bioreactors
AIChE Journal, EarlyView.Abstract Developing reliable predictive models for mammalian cell bioreactors, particularly Chinese hamster ovary (CHO) cultures widely used in biopharmaceutical manufacturing, remains challenging due to severe data scarcity in industrial‐scale reactors.
Muyang Li, Ming Xiao, Zhe Wu
wiley +1 more source
Integrating Machine Learning and Dynamic Bayesian Networks to Identify the Factors Associated with Subsequent Intrapulmonary Metastasis Classification After Initial Single Primary Lung Cancer. [PDF]
Cancers (Basel)Liu W +6 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee +3 more
wiley +1 more source
Bayesian networks and structural equation models reveal genetic causal relationships between productivity, defense, and climate-adaptability traits in interior lodgepole pine. [PDF]
G3 (Bethesda)Cappa EP +7 more
europepmc +1 more source
Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion
Advanced Intelligent Discovery, EarlyView.This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia +1 more
wiley +1 more source
Using Dynamic Causal Bayesian Networks to Assess the Role of Patient-Centered Care and Individual-Level Barriers on Viral Suppression Changes Among a Cohort of People with HIV. [PDF]
AIDS BehavTrepka MJ +9 more
europepmc +1 more source