Distributed Acoustic Sensing (DAS) transforms optical fiber cables into dense networks of virtual sensors for long-range monitoring. A comprehensive review examines DAS technologies, signal processing methods, challenges, and applications spanning geophysics, infrastructure, transportation, energy, environmental monitoring, security, and aerospace. Advances in photonics, artificial intelligence, and data standardization could improve sensing range, resolution, affordability, and portability, expanding DAS into emerging civilian applications such as healthcare wearables and smart cities.

Delivering high-definition television over local 5G networks could help modernize cable TV in buildings where installing fiber-optic cables is impractical. However, since lost data cannot be retransmitted in 5G multicast broadcasting, temporary video freezing is common. Now, researchers from Japan have developed a lightweight AI model that predicts wireless conditions in advance and proactively adjusts transmission settings, enabling stable, real-time video delivery via 5G broadcasting. Their work helps bridge the information gap in underserved areas. 

In a recent study, researchers from Japan analyzed data from over 73,000 mother–child pairs and found that a mother’s negative or indifferent feelings upon learning they are pregnant were associated with a higher likelihood of developmental delays at age three. The study also revealed social support substantially reduced this risk, highlighting the need for timely interventions.

Accurate and efficient isolation and recovery of interest cells allow detailed analyses of cellular function, growth and culture, and gene-expression profiling of cells within heterogeneous populations. Researchers have developed a novel microfluidic surface-enhanced Raman scattering (SERS) chip capable of performing deep-learning-assisted, label-free single-cell identification and sorting. The results could be used to guide future research on the design and development of non-invasive, efficient cell-sorting techniques with potential applications in clinical diagnostics and precision medicine.

Eight technologists building five Singapore-based deep-tech start-ups have been selected for the inaugural cohort of Activate Global Fellows – Singapore, a national fellowship hosted by Nanyang Technological University, Singapore (NTU Singapore) to support promising science and technology ventures develop into globally competitive companies.

A research team comprising Associate Professor FUNAKOSHI Yohei, Associate Professor YAKUSHIJIN Kimikazu, Professor MINAMI Hironobu, and Associate Professor OHJI Goh of Kobe University, together with graduate student MASUDA Genki, then-graduate student IIZUMI Shunsuke, Associate Professor OHUE Masahito, and colleagues at the Institute of Science Tokyo, has developed a new analysis method, LM-QASAS. This method combines antibody language models with B-cell receptor (BCR) repertoires(*1) collected before and after vaccination to identify and track candidate BCR sequences that are likely to have responded to an immune stimulus, without using a database of known antigen-specific antibodies. LM-QASAS extends QASAS(*2; Quantification of Antigen-Specific Antibody Sequence), a method for quantitatively evaluating immune responses to a specific antigen through antigen-receptor repertoire analysis

This study develops a novel “Panning for Gold” framework that integrates Koopman analysis with clustering algorithms to identify representative water quality dynamic data for wastewater treatment plants (WWTPs). Using a full-scale Anaerobic-Anoxic-Oxic process in southern China as a case study, the research demonstrates that machine learning models can maintain predictive accuracy with 43.59% less training data, establishing a low-data-requirement paradigm for efficient water system management.