This content describes a Physics-Informed Neural Network (PINN) that fuses satellite sea surface temperature (SST) with sparse in-situ loggers to resolve depth-resolved coral reef thermal fields. The model effectively corrects for overestimations of subsurface thermal stress, achieving high accuracy even with minimal training data.
This study develops a machine learning framework to predict, forecast, and control NOx emissions in cement manufacturing, a major source of industrial air pollution. The framework utilizes large-scale operational data from multiple plants, significantly improving prediction accuracy and enabling proactive operational adjustments to mitigate pollution.
This content reflects on observing a house as a living, breathing organism using CO2, light, and sound data. The analysis aims to understand the "metabolic heartbeat" of the Siuntio Fort through an intelligent perspective.
Artificial Intelligence can predict pollution before it reaches dangerous levels by analyzing vast volumes of environmental and operational data. This includes information on weather, traffic, and industrial emissions to identify patterns and correlations.
This article presents five app ideas for tree care, focusing on moving beyond simple planting maps to address survival rates or transparency. One of the proposals is an "AI Tree Doctor," highlighting the relevance of artificial intelligence in environmental monitoring.