Researchers from Shanxi University and Nanjing University have demonstrated on-chip two-mode squeezed light at audio frequency as low as 60 Hz, bringing integrated quantum noise reduction into the audio-frequency band. A weak, orthogonally polarized electro-optic reference comb stabilizes the homodyne measurement phase without placing a bright locking tone at the quantum frequencies. The experiment observed approximately 1 dB of squeezing from 60 Hz to 5 kHz, reconstructed the two-mode covariance matrix, and verified entanglement using the positive partial transposition criterion.

The central north Amazon, with extensive areas of high forest cover, natural savannas and vast and important indigenous territories, was not previously considered as being the most affected by climate change.

However, this region, which is considered critical to Amazonia’s cultural and biodiversity wealth has unexpectedly found itself at the forefront of rapid growth in climate extremes, a new study reveals.

Findings, published today in Communications Earth & Environment, identify Amazonia as experiencing rapid growth in extreme temperatures and water stress – with 10% of the basin, an area larger than 700,000 square kilometres (bigger than Afghanistan), witnessing increases in extreme temperatures during the dry season of at least 0.75°C a decade and more than 3.22°C since 1981.

A nationwide survey in Japan suggests cultural worldviews are more strongly linked to support for climate policies than to support for biodiversity conservation measures. While hierarchical worldviews were linked to lower perceived climate risks and weaker support for climate measures, these associations were weaker for climate-related biodiversity conservation approaches and not clearly observed for stand-alone conservation policies, pointing to potential common ground.

Associate Professor Kenta Kasai of Science Tokyo has developed a high-rate quantum low-density parity-check (LDPC) code by adapting design theory from classical communications and storage. By imposing orthogonality only where quantum mechanics requires it, the method retains the freedom used to control connection degrees, randomness, and short loops in classical LDPC codes. Overseas teams have already extended the design to neutral-atom hardware and broader code frameworks, referring to the resulting family as “Kasai codes.”

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.

HieraScaffold is an AI framework that could help autonomous-driving and robotics teams create large, dynamic simulation environments with less reliance on costly real-world LiDAR data collection.

Finding better materials to store hydrogen safely and efficiently is key to clean energy, but AI models trained on incomplete data can recommend candidates that don't actually work in practice. An international team led by Tohoku University, drawing on insight from 69 researchers, has proposed a "physics-aware" framework that combines thermodynamic constraints, reproducible data, and real experimental feedback to make AI-guided materials discovery more reliable. The roadmap could help accelerate the search for practical hydrogen storage solutions to support future clean-energy systems.