Results 131 to 140 of about 39,124 (245)

Prediction of Bandgap and Key Feature Analysis of Lead-Free Double Perovskite Oxides Based on Deep Learning. [PDF]

Molecules
Wang B, Wang J.
europepmc   +1 more source

Energy Landscapes in Chemical Reactions and Transport

ChemPhysChem, Volume 26, Issue 6, March 15, 2025.
Kinetics/dynamics of chemical reactivity and transport of chemical species in a solid are both determined by the energy landscape in which they take place. Discussing common grounds but also distinct differences may help in advancing the understanding in both fields.
Karl‐Michael Weitzel
wiley   +1 more source

A multi-modal data fusion and real-time monitoring on stroke risk prediction using federated learning. [PDF]

PLoS One
K RS, P MK.
europepmc   +1 more source

Bridging Theory and Prediction: A Hybrid SEM and Machine Learning Approach to Optimize Lean Construction for Megaproject Sustainability in China

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Construction megaprojects, large‐scale, complex, and capital‐intensive, are particularly prone to inefficiencies, cost overruns, delays, and environmental degradation due to fragmented workflows, stakeholder misalignment, and resource intensity.
Abdelazim Ibrahim, Tarek Zayed, Zoubeir Lafhaj, Ahmed Maged, Ahmed Farouk Kineber, Jingchao Yang   +5 more
wiley   +1 more source

An automated decision making framework for modern vehicles CO₂ emissions using multi modal engine telemetry and feature interpretability. [PDF]

Sci Rep
Saraswat SK, Abdullah M, Habelalmateen MI, Vivek V, Sahu PK, Pant R, Sharma S, Shojonov M, Madaminov B.   +8 more
europepmc   +1 more source

AI‐Driven Precision Annealing for High Performance Fe‐Based Amorphous Alloys

ENERGY &ENVIRONMENTAL MATERIALS, EarlyView.
The four stages of the research process are as follows: First, data is collected and a database is constructed. This is followed by feature selection and analysis, then the establishment of machine learning models, and finally formulation design and preparation.
Yichuan Tang, Shaopeng Liu, Silong Li, Ruonan Ma, Peinan Li, Zheng Wang, Kun Wang, Kaiyan Cao, Sidan Ding, Chao Zhou, Fanghua Tian, Sen Yang, Minxia Fang, Yin Zhang   +13 more
wiley   +1 more source

Uncertainty aware hybrid learning framework for fast and safe charging of lithium-ion batteries using multi-fidelity observers. [PDF]

Sci Rep
Parimala CHH, Varma PS, Kumar RS, Singh AR, Mohapatra B, Addis HK.   +5 more
europepmc   +1 more source

Innovations for Stakeholder Engagement in Water Governance: A Systematic Literature Review From a Sustainability Transition Perspective

Environmental Policy and Governance, EarlyView.
ABSTRACT Sustainable and equitable governance of water resources is among the most pressing global challenges, emphasising the need for innovations towards transitions in the water sector. Innovations for stakeholder engagement can contribute to such transitions through public participation and cross‐sectoral collaboration.
Anne Ellermann, Gül Özerol, Joanne Vinke‐de Kruijf, César Casiano Flores, Vuokko Laukka, Ulf Stein, Suvi Sojamo, Rob Collins   +7 more
wiley   +1 more source

Explainable machine learning for stroke risk prediction: a comparative study with SHAP-based interpretation. [PDF]

Front Neurol
Tang X, Tang M, Liu W, Cui S.
europepmc   +1 more source

AI‐driven circular economy optimization in waste management: A review of current evidence

Environmental Progress &Sustainable Energy, EarlyView.
Abstract The integration of artificial intelligence (AI) and machine learning (ML) in waste management has the potential to significantly advance circular economy objectives by enhancing efficiency, reducing waste, and optimizing resource recovery. However, realising these benefits depends on addressing significant technical, economic, and systemic ...
David Bamidele Olawade, Ram Narayan Yadav, James O Ijiwade, Ojima Z. Wada   +3 more
wiley   +1 more source