A study by researchers at Queen Mary University of London and University College London has found that humans have a form of remote touch, or the ability to sense objects without direct contact, a sense that some animals have.  

Human touch is typically understood as a proximal sense, limited to what we physically touch. However, recent findings in animal sensory systems have challenged this view. Certain shorebirds, such as sandpipers and plovers, use a form of “remote touch” to detect prey hidden beneath the sand (du Toit et al. 2020; de Fouw et al. 2016). Remote touch allows the detection of objects buried under granular materials through subtle mechanical cues transmitted through the medium, when a moving pressure is applied nearby. 

  The study in IEEE International Conference on Development and Learning (ICDL) investigated whether humans share a similar capability. Participants moved their fingers gently through sand to locate a hidden cube before physically touching it. Remarkably, the results revealed a comparable ability to that seen in shorebirds, despite humans lacking the specialized beak structures that enable this sense in birds. 

Development in artificial intelligence has paved the path for the integration of computational pathology in clinical workflow, improving the accuracy and reducing the workload for medical practitioners. In recent times, Foundation Models (FMs), trained on large-scale, unlabelled datasets, are considered more suitable than traditional models for diverse clinical tasks. In a review study published in the Chinese Medical Journal, researchers highlight the advancements in pathological FMs and discuss their application in precision oncology.

Dark matter accounts for approximately 85% of the universe’s total mass, yet its “invisibility” continues to challenge our understanding of physics. While the Standard Model has successfully described the structure of the visible universe, its limitations have driven scientists to explore ultralight exotic bosons—such as axions and dark photons—as motivative candidates for dark matter. Theoretical studies suggest that such new bosons could mediate exotic spin-dependent interactions beyond four fundamental forces, providing new avenues for detecting ultralight dark matter. However, terrestrial exotic-interaction searches have long been constrained by a fundamental trade-off: enhancing the signal of exotic spin interactions requires simultaneously increasing both the number of polarized spins and relative velocity, parameters that are inherently inversely coupled under laboratory conditions, leaving vast regions of theoretical parameter space unexplored.

Professor Xinhua Peng and Professor Min Jiang from the University of Science and Technology of China, in collaboration with multiple research institutions, have proposed the SQUIRE (Space-based QuantUm sensing for Interaction and exotic bosons Research Exploration) program—a space-based dark matter detection project. For the first time internationally, SQUIRE plans to deploy ultrasensitive quantum sensors aboard the China Space Station to search for potential exotic interactions mediated by dark matter candidate particles between the Earth’s geoelectron spins and the sensor spins. The scheme is projected to improve detection sensitivity by more than 7 orders of magnitude compared to terrestrial experiments. Furthermore, SQUIRE is expected to pave the way for a “space-ground integrated” quantum sensing network, opening new pathways for dark matter exploration in deep space. This paper was published on September 22 in National Science Review under the title “Quantum Sensors in Space: Unveiling the Invisible Universe.”

An international team led by Japan’s National Institute of Polar Research discovered that large-scale East Antarctic ice loss around 9,000 years ago was amplified by a “cascading positive feedback” between meltwater and ocean circulation. Sediment records and ocean–climate modeling show that meltwater from other Antarctic regions strengthened warm deep-water inflow, accelerating ice-shelf collapse and inland thinning. The finding highlights how Antarctic ice melt can self-reinforce under ongoing global warming.

Seoul National University College of Engineering announced that a research team led by Professor Hyun Oh Song from the Department of Computer Science and Engineering has developed a new AI technology called “KVzip” that intelligently compresses the “conversation memory” of large language model (LLM)-based chatbots used in long-context tasks such as extended dialogue and document summarization.

Recently, a research team from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences, has developed a new deep learning method that improves the classification accuracy of mixed microplastics in infrared spectroscopy to 98%.

POSTECH Professor Sanguk Kim's team develops a machine learning model to predict drug toxicity based on genotype-phenotype differences between preclinical models and humans.