World’s first carbon fiber metro train in Qingdao

http://en.people.cn/n3/2025/0110/c90000-20264742.html

The world’s first carbon fiber metro train named “CETROVO 1.0 Carbon Star Express began passenger service on the Metro Line 1 of Qingdao, Shandong Province. The debut of the carbon fiber metro train marks a groundbreaking upgrade in China’s metro train lightweight technology. The carbon fiber metro train is approximately 11 percent lighter, with operational energy consumption reduced by 7 percent. And each train can reduce carbon dioxide emissions by 130 tons annually, the CCTV report noted.

The key load-bearing structures of the carbon fiber metro train, such as the car body and bogie frame, are made from carbon fiber composite materials. And this design offers multiple technical advantages, including being lighter and more energy-efficient, having higher strength, better environmental adaptability, and lower operation and maintenance costs throughout its life cycle.

Carbon fiber has advantages such as being lightweight, high-strength, fatigue-resistant, and corrosion-resistant. Its strength is more than five times that of steel, while its weight is less than a quarter of steel, making it an excellent material for lightweight rail vehicles, The use of carbon fiber materials not only enhances the strength of the car body, providing greater impact resistance and extending the structural lifespan, but also improves the vehicles’ vibration reduction and isolation, resulting in smoother operation, reduced noise, and a more comfortable ride.

The Qingdao Metro Line 1 spans approximately 60 kilometers and has 41 stations. It serves as a major north-south backbone line in Qingdao.

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A humanoid robot training center in Qingdao improves robot efficiency

https://en.people.cn/n3/2026/0324/c90000-20439477.html

At the Qingdao Humanoid Robot Data Training Center in Laoshan District of Qingdao, humanoid robots are trained for jobs such as intelligent industrial manufacturing, smart home, and commercial services. Data collectors here control robots to complete specific tasks like logistics sorting, supermarket restocking, kitchen operations, and component assembly. Through thousands of repetitions and trials, massive amounts of motion data are generated, endowing robots with a smarter “intelligent brain,” and helping humanoid robots enter all walks of life to serve thousands of households.

RamEx: high-throughput microbial ramanome analyses with accurate quality assessment

https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

RamEx removes spectral noise in Raman-based microbial cell sorting

http://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml

https://link.springer.com/article/10.1186/s40168-026-02339-3

Scientists from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics.

RamEx streamlines the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. An Iterative Convolutional Outlier Detection (ICOD) algorithm tackles spectral noise in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform’s performance was validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in genetically identical yeast cells by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

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