Results 191 to 200 of about 358,351 (306)

Food Tastes in the United States: Convergence or Divergence?

open access: yesAgribusiness, EarlyView.
ABSTRACT This study investigates how food consumption tastes have changed in recent decades across the United States. Using NielsenIQ data for over 77 million transactions, there is evidence of divergence in food tastes across regions from 2007 to 2016 and across households of different income, education, and race/ethnicity groups.
Michael DeDad
wiley   +1 more source

Vendor Types, Attendance, Experience and Sales 2019–2021: Evidence From Five Rural Oregon Farmers Markets

open access: yesAgribusiness, EarlyView.
ABSTRACT Farmers markets provide a direct‐to‐consumer marketing path for farmers and small businesses, facilitating customer discovery and product refinement. This paper explores farmers markets as a business incubator, with a focus on beginning vendors and resilience to a shock, namely, COVID‐19 market restrictions.
Mallory L. Rahe   +2 more
wiley   +1 more source

Food Prices and Inflation Expectations in New Zealand

open access: yesAgribusiness, 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

The Power of Monotony. [PDF]

open access: yesJ Orthop Case Rep
Sancheti P, Shyam A.
europepmc   +1 more source

Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

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