Results 271 to 280 of about 810,945 (328)

Does ESG Matter for Unlisted Companies in the Agri‐Food Industry? Evidence From Japan's Unlisted Agri‐Food Companies

open access: yesAgribusiness, EarlyView.
ABSTRACT While ESG (environmental, social, and governance) is emphasized among listed companies for their stakeholders and ESG disclosures, ESG engagement among unlisted companies has been rarely examined due to data limitations. This is particularly problematic for the agri‐food industry that has significant impacts on the environment and consists ...
Ying Wang, Satoru Shimokawa
wiley   +1 more source

Return and Volatility Spillovers Among Major Cotton Markets

open access: yesAgribusiness, EarlyView.
ABSTRACT This study explores return and volatility transmission among major cotton markets. Several events have disrupted cotton supply and demand in recent years, leading to heightened price volatility and significant shifts in market interconnections.
Susmitha Kalli   +3 more
wiley   +1 more source

Two Trees: Asset Price Dynamics Induced by Market Clearing

open access: bronze, 2003
John H. Cochrane   +2 more
openalex   +1 more source

A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study develops six machine learning models (k‐nearest neighbors, support vector regression, decision tree, random forest, CatBoost, and backpropagation neural network) to predict SiNx deposition rates in plasma‐enhanced chemical vapor deposition using hybrid production and simulation data.
Yuxuan Zhai   +8 more
wiley   +1 more source

Solving Data Overlapping Problem Using A Class‐Separable Extreme Learning Machine Auto‐Encoder

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The overlapping and imbalanced data in classification present key challenges. Class‐separable extreme learning machine auto‐encoding (CS‐ELM‐AE) is proposed, which is an enhancement of ELM‐AE that better handles overlapping data by clustering points from the same class together. Applying oversampling addresses imbalanced data.
Ekkarat Boonchieng, Wanchaloem Nadda
wiley   +1 more source

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