Results 181 to 190 of about 137,924 (252)

The Role of Coordination Mechanisms and Transaction Costs Promoting Sustainability Performance in Agri‐Food Supply Chains: Evidence From Ecuador

Agribusiness, EarlyView.
ABSTRACT Social tensions and resource depletion pose significant challenges to the agri‐food sector, highlighting the need for coordinated strategies to ensure sustainability in supply chains. Despite its critical importance, the relationship between coordination mechanisms and sustainability performance remains underexplored.
Carlos Moreno‐Miranda, Liesbeth Dries
wiley   +1 more source

Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks. [PDF]

Sci Rep
Prabha KL, Mengash HA, Alqahtani H, Allafi R.   +3 more
europepmc   +1 more source

MSPM: A modularized and scalable multi-agent reinforcement learning-based system for financial portfolio management. [PDF]

PLoS One, 2022
Huang Z, Tanaka F.
europepmc   +1 more source

The Role of Actual and Purported Origin in e‐Commerce Wine Pricing: Evidence From Italian and French Names on Labels

Agribusiness, EarlyView.
ABSTRACT The origin of a product, if associated with good quality, can contribute to building a positive collective reputation, leading to a potential price premium. However, it is conceivable that a producer markets a product by evoking symbols, images, words, and values typical of places other than where it was designed or produced, creating a ...
Annalisa Caloffi, Amir Maghssudipour, Gianluca Stefani   +2 more
wiley   +1 more source

A multi-agent reinforcement learning based approach for automatic filter pruning. [PDF]

Sci Rep
Li Z, Zuo X, Song Y, Liang D, Xie Z.
europepmc   +1 more source

Attention-Based Fault-Tolerant Approach for Multi-Agent Reinforcement Learning Systems. [PDF]

Entropy (Basel), 2021
Gu S, Geng M, Lan L.
europepmc   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control. [PDF]

Sensors (Basel)
Bokade R, Jin X.
europepmc   +1 more source

A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water‐Gas Shift Catalysis on MXenes

Advanced Intelligent Discovery, EarlyView.
Machine learning predicts activation energies for key steps in the water‐gas shift reaction on 92 MXenes. Random Forest is identified as the most accurate model. Reaction energy and reactant LogP emerge as key descriptors. The approach provides a predictive framework for catalyst design, grounded in density functional theory data and validated through ...
Kais Iben Nassar, Tiago L. P. Galvão, José D. Gouveia, José R. B. Gomes   +3 more
wiley   +1 more source

AI-driven multi-agent reinforcement learning framework for real-time monitoring of physiological signals in stress and depression contexts. [PDF]

Brain Inform
Shaik T, Tao X, Li L, Xie H, Dai HN, Zhao F, Yong J.   +6 more
europepmc   +1 more source