Results 131 to 140 of about 68,989 (275)

STELLAR‐CB: Synthetic Temporal LSTM for Livestock Activity Recognition—Cow Behaviour

open access: yesVeterinary Medicine and Science, Volume 12, Issue 4, July 2026.
This study introduces a novel framework combining LSTM networks with SMOTE to address class imbalance in precision livestock farming. It improves the detection of rare behaviours in livestock, achieving state‐of‐the‐art performance while reducing computational overhead, offering a practical, breed‐agnostic solution for enhancing automated behaviour ...
Ghufran Ahmed   +9 more
wiley   +1 more source

An optimization model for the energy management of the network of tanks in a drinking water distribution system

open access: yesInternational Transactions in Operational Research, Volume 33, Issue 4, Page 2709-2730, July 2026.
Abstract “L'eau c'est la vie” is a well‐known French expression for “water is life,” which reflects the fact that water is undoubtedly the most vital resource in the world. The main mission for water utility companies is to convey and distribute water that is of acceptable quality to satisfy the demand of the population at any time of the day.
Franklin Djeumou Fomeni   +4 more
wiley   +1 more source

Diffusion–Model–Driven Discovery of Ferroelectrics for Photocurrent Applications

open access: yesAdvanced Science, Volume 13, Issue 31, 4 June 2026.
We developed a diffusion model–based generative AI and high‐throughput screening framework that accelerates the discovery of photovoltaic ferroelectrics. By coupling AI driven crystal generation with machine learning and DFT screening, we identified Ca3P2 and LiCdP as new ferroelectric materials exhibiting strong polarization, feasible switching ...
Byung Chul Yeo   +3 more
wiley   +1 more source

Optimal exact designs of experiments via Mixed Integer Nonlinear Programming

open access: yesStatistics and computing, 2019
B. Duarte, J. Granjo, W. Wong
semanticscholar   +1 more source

Graph‐based imitation and reinforcement learning for efficient Benders decomposition

open access: yesAIChE Journal, Volume 72, Issue 6, June 2026.
Abstract This work introduces an end‐to‐end graph‐based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural network, which takes as input a bipartite graph representation of the master problem and proposes a candidate solution.
Bernard T. Agyeman   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy