This review covers recent advances in artificial intelligence (AI) for aquatic species identification and conservation, systematically summarizing the current applications and challenges of machine learning algorithms in image analysis, acoustic identification, and ecological threat detection. It highlights that AI models such as convolutional neural network (CNN) and recurrent neural network (RNN) have demonstrated significantly higher accuracy and scalability in species classification, habitat monitoring, and environmental threat detection compared to traditional ecological survey methods. Additionally, through bibliometric analysis, the study identifies global research trends and interdisciplinary collaboration patterns, emphasizing the importance of strengthening cross-disciplinary cooperation, establishing standardized protocols, and developing open data platforms to support sustainable aquatic ecosystem conservation.

This study proposes an automatic liver segmentation method for computed tomography (CT) images based on an improved ResUNet, which integrates the advantages of UNet and ResNet architectures to achieve high-precision segmentation on 128×128pixel images. The model removes batch normalization layers and incorporates residual blocks, combined with data augmentation techniques, ultimately achieving excellent metrics such as a Dice coefficient of 93.08% and accuracy of 98.57%. Surpassing traditional methods, this approach provides efficient and reliable technical support for liver disease diagnosis.

As hydrogen infrastructure is rolled out in the EU, refuelling stations must be distributed according to the same principle in all countries. But now a study from Chalmers University of Technology in Sweden points to shortcomings in EU regulations. Using an advanced model, the researchers show that the distribution of refuelling stations may both be incorrectly dimensioned and lead to losses of tens of millions of euros a year in some countries.

Scientists have developed lightning-produced ammonia in gas form – a new, more efficient method that brings us closer to sustainable production of ammonia and transition to a hydrogen-based economy. 

New research shows that the more we interact with robots, the more human we perceive them to become.

The team carried out a series of experiments with a box-shaped robot called Cozmo. They found that playing games with this little robot to ‘break the ice’ helped bring out its human side.

The implications are significant for the future of robotics. As robots take on roles from care-giving to customer service, designing interactions that promote social engagement could make them more acceptable to humans.