Environmental DNA (eDNA) metabarcoding is transforming biodiversity monitoring by allowing scientists to identify fish species from diverse aquatic ecosystems. To assess reliability of the method, researchers from South Korea analyzed 59 mock community datasets from 30 independent studies, comparing the performance of universal fish primers and reference databases. They found that eDNA metabarcoding is highly effective for detecting fish diversity, but quantitative estimates of fish biomass remain challenging due to methodological biases and database limitations.

Brake-by-wire systems are becoming a core technology for intelligent electric vehicles, but their performance depends heavily on a compact, reliable and responsive booster motor. A study in Green Energy and Intelligent Transportation presents a structured design method for the booster motor used in brake-by-wire systems, combining motor selection, electrical and mechanical design, simulation optimization and prototype verification.

Researchers have developed a lightweight fault-diagnosis framework for high-speed train bogies that is designed for edge deployment while still maintaining strong cross-domain diagnostic performance. The study addresses a persistent challenge in railway intelligence: how to run deep learning models close to the train or infrastructure, where computing resources are limited, without losing accuracy when operating conditions change.

Dyes are used to colour papers, leather products, garments, and are also employed in the rubber and cosmetic industries. But what happens after these dyes have served their purpose? Billions of tons of dye-containing wastewater enter water systems every year, eventually rendering them unfit for human and animal use. They can also lead to serious health issues such as breathing problems, jaundice, and cancer. As an effort to tackle this issue, researchers have developed advanced materials, which act like microscopic sponges, as a promising solution to industrial wastewater treatment. 

Researchers have developed a real-time vehicle-tracking framework that can follow traffic across large roadside sensor networks with low latency and meter-level accuracy, a step that could help support safer and more responsive vehicle-road-cloud integration systems.