Undiagnosed trauma is a silent driver of chronic illness, often missed by healthcare systems. This study introduces Emotional Memory Images (EMIs) as subconscious imprints formed in overwhelming moments that continue to affect mental and physical health, offering a rapid, non-invasive method to clear them.

This article explores the potential of large language models (LLMs) in reliability systems engineering, highlighting their applications in improving industrial efficiency and flexibility. It also examines the challenges, including data deficiencies and complexity, and suggests future directions for integrating LLMs into complex engineering processes.

A new study in Engineering introduces a novel MRI technique enabling real-time, artifact-free navigation of magnetic robots. Using a multi-frequency dual-echo sequence, researchers achieved a 30-millisecond repetition time, allowing precise control and high-resolution imaging. Demonstrations in maze, phantom vessel, and in vivo trials highlight its potential for minimally invasive therapies.

New review shows that guiding pollutant motion with electric fields and 3D-printed hierarchical pores can slash fan energy and double removal rates, offering a practical blueprint for next-generation, low-resistance air purifiers.

Plants rely on efficient internal transport systems to distribute essential mineral nutrients, yet how this distribution is spatially organized within complex plant structures remains poorly understood. 

Structural variations (SVs)—large-scale changes in DNA sequence—play a crucial role in shaping traits such as yield, quality, and environmental adaptation in crops. 

 A methodology to enhance the energy density of supercapacitors based on a V₄C₃T_Z MXene has been developed via enhancement of the operating electrochemical window using a 17.5 molal LiBr/H₂O highly concentrated electrolyte.

A team of Chinese researchers has developed an AI-based modeling approach that revolutionizes the prediction of complex nonlinear dynamics in Kerr resonators. By leveraging recurrent neural networks (RNNs)—specifically gated recurrent units (GRUs)—and a hybrid convolutional neural network (CNN)-GRU model for complex scenarios, the team achieved nearly 20x faster simulations than traditional methods, while maintaining high accuracy. The work paves the way for faster design of next-generation optical systems, from optical memories to all-optical computers.