Researchers at Science Tokyo have developed a method to dissolve previously insoluble porous aromatic polymers (PAPs) in water by encapsulating them in aromatic micelles. These 100 nm-sized particles, which normally function only in solid form, can now incorporate hydrocarbons and dyes in solution, forming unique multi-component materials. The technique uses a simple grinding and sonication process, and enables particle size control via centrifugation and filtration. Notably, the presence of hydrocarbons enhances the fluorescence of the embedded dyes up to ninefold. This breakthrough introduces a new class of water-soluble, polycavity materials with potential applications in sensing, storage, and catalysis—opening up new possibilities beyond conventional single-cavity molecular systems.

A research team has designed and implemented a group motivational interviewing system using multiple chatbots to support premenstrual syndrome (PMS), a common disorder among women. The system consists of a facilitator bot and two peer bots, and simulates a group counseling environment for PMS management. These findings provide valuable insights into the use of multiple chatbots for addressing mental health issues.

Despite advances in machine vision, processing visual data requires substantial computing resources and energy, limiting deployment in edge devices. Now, researchers from Japan have developed a self-powered artificial synapse that distinguishes colors with high resolution across the visible spectrum, approaching human eye capabilities. The device, which integrates dye-sensitized solar cells, generates its electricity and can perform complex logic operations without additional circuitry, paving the way for capable computer vision systems integrated in everyday devices. 

Human-AI interactions are well understood in terms of trust and companionship. However, the role of attachment and experiences in such relationships is not entirely clear. In a new breakthrough, researchers from Waseda University have devised a novel self-report scale and highlighted the concepts of attachment anxiety and avoidance toward AI. Their work is expected to serve as a guideline to further explore human-AI relationships and incorporate ethical considerations in AI design.

A team of researchers used a machine learning model to predict unknown chemical pathways, and bring us one step closer to efficiently storing next-generation hydrogen fuels.

Automated spoken English assessment tests have gained popularity for their well-documented advantages, such as ease of administration and cost efficiency. However, they often focus on single modalities, such as text or audio, and have limited relevance in interactive settings. Now, in a new research study, researchers from the Japan Advanced Institute of Science and Technology have developed a multi-output learning framework that can simultaneously assess multiple aspects of spoken English proficiency.

An overactive immune response can damage the kidneys but also presents a promising target for treatments to slow or prevent disease progression.

A team led by Professor Masakatsu Murakami has developed a novel concept called micronozzle acceleration (MNA). By designing a microtarget with tiny nozzle-like features and irradiating it with ultraintense, ultrashort laser pulses, the team successfully demonstrated—through advanced numerical simulations—the generation of high-quality, GeV-class proton beams: a world-first achievement.

Turbulence—where flows and vortices intricately intertwine—is a fundamental phenomenon found across nature, from our everyday surroundings to the cosmos. In particular, high-temperature fusion plasmas exhibit highly complex turbulence, driven by the coupled fluctuations of multiple physical quantities such as density, temperature, and magnetic fields. A research team led by  Dr Go Yatomi of the National Institute for Fusion Science (a graduate student at SOKENDAI at the time of submission) and Associate Professor Motoki Nakata of Komazawa University (also a visiting researcher at RIKEN) has developed a novel method to analyze turbulent states and interactions from the perspective of "information," drawing inspiration from mathematical frameworks in quantum mechanics, such as information entropy. This approach enables the discovery of previously overlooked turbulence regimes in plasmas and reveals essential nonlinear interactions between multi-scale vortices and flows—interactions that conventional energy-based analyses often miss. The study also proposes potential applications of the method to experimental diagnostics of turbulence and fluctuations. Looking ahead, this technique is expected to extend beyond plasma physics, offering new insights into complex systems in atmospheric and oceanic sciences, as well as in social dynamics, where complex flows and multi-variable interactions also play critical roles.