Results 151 to 160 of about 2,961,847 (275)

What Explains Racial Differences in Farm Succession Planning? Evidence From Decomposition and Counterfactual Analysis

open access: yesAgribusiness, EarlyView.
ABSTRACT This paper examines racial differences in farm succession planning using survey data from Tennessee farmers. While observed differences between White and Black farmers are small, decomposition and counterfactual analyses show that structural characteristics, particularly farm size, would predict higher succession planning among White farmers ...
Abdelaziz Lawani   +2 more
wiley   +1 more source

Economic Impact of Sustainability in Wine Sector: A Systematic Literature Review

open access: yesAgribusiness, EarlyView.
ABSTRACT Sustainability has become a key issue in the wine sector, raising questions about its compatibility with firms' economic performance. Despite growing attention to environmental and social practices, evidence on their economic implications remains fragmented.
Valentina Di Chiara   +2 more
wiley   +1 more source

The Effect of Sustainability Information on Consumers' Preferences for Fungus‐Resistant Grape Wine

open access: yesAgribusiness, EarlyView.
ABSTRACT The demand for sustainable food production is intensifying the need to reduce the environmental and social impacts of pesticide use in agriculture. In viticulture, where dependence on pesticides is high because grapevines are susceptible to fungal diseases, fungus‐resistant grape varieties (PIWI) offer a promising strategy to reduce chemical ...
Daniel Vecchiato   +2 more
wiley   +1 more source

Machine learning driven many‐objective moving horizon scheduling optimization

open access: yesAIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

open access: yesAdvanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Spatially Informed Feature Selection and Machine Learning in Matrix‐Assisted Laser Desorption/Ionization Imaging for Cohort‐Scale Molecular Tissue Phenomics in Glioblastoma

open access: yesAdvanced Intelligent Discovery, EarlyView.
Matrix‐assisted laser desorption/ionization imaging‐based identification of reliable small molecule markers across heterogeneous glioblastoma cohorts is challenging with intensity‐only methods. We present spatially informed feature selection (SIFS), a spatially informed framework that prioritizes molecules consistently colocalizing with histopathology.
Shad A. Mohammed   +15 more
wiley   +1 more source

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