Scientists from the Institute of Industrial Science, The University of Tokyo, have used in situ confocal microscopy to study colloidal gels. They found that different local particle arrangements uniquely modulated the properties of the gel, with tetrahedra arresting motion and pentagonal bipyramid clusters imparting solidity.
Researchers developed a machine-learning algorithm to predict the density of states within an organic molecule using core-loss spectroscopy data. The model achieved improved accuracy by excluding tiny molecules and adding specific noise to the data.
Researchers from The University of Tokyo have created a machine that can recharge N95 respirators and surgical masks to 97% efficiency. By applying a uniform voltage distribution, the device restores the mask's electrostatic charge, increasing its effectiveness.
A study reveals that climate change will lead to increased flood risks globally, with 1.86 billion people living in areas prone to extreme flooding. The research team developed a new approach to create high-resolution future flood hazard maps using climate change simulations and compared the results with historical maps.
Researchers at the Institute of Industrial Science at The University of Tokyo have found that phonons in isotopically pure carbon can behave like a fluid, allowing for faster heat conduction. This phenomenon, known as phonon Poiseuille flow, has implications for cooling sensitive computer processors and improving efficiency in electron...
Researchers found that simultaneous learning of two tasks enhances a deep-learning model's performance on retrieving precipitation information from satellite data. The new framework uses multi-task learning to improve current estimates of precipitation, outperforming existing approaches.
Researchers at The University of Tokyo have developed a programmable gate driver for solid-state electronic transistor switches, reducing switching loss under changing input current and temperature fluctuations. The device includes automatic timing control, allowing for single-chip integration and real-time control.
A team of researchers from The University of Tokyo created a computer simulation to study the phase separation of counter-rotating particles in a fluid. They found that nonlinear turbulent effects lead to the sudden separation of particles into regions of clockwise and counterclockwise collections.
Researchers at The University of Tokyo have developed a cheap and simple method to bond polymers to galvanized steel, resulting in lightweight and durable materials. The process involves pre-treating the steel with an acid wash and dipping it in hot water, creating nanoscale needle structures that allow for strong mechanical linkages.
By incorporating hydrodynamics into their models, the researchers improved predictions of final structures compared to conventional computational models. This work may lead to the development of smart materials with controllable properties in response to external conditions.
Researchers from the University of Tokyo simulated fracture in amorphous solids to better understand material fatigue. They found that the critical strain for irreversible deformation is the same for both fatigue and monotonic fractures.
Sensory neurons in human skin have been found to regulate melanocytes, influencing pigmentation and cell survival. The study identified a protein called RGMB as a key factor promoting melanocyte survival and darkness.
A team from The Institute of Industrial Science at The University of Tokyo has developed a new platform that uses organorhodium(III) phthalocyanine complexes to achieve the combination of traits necessary for photodynamic therapy. The new system shows toxicity to HeLa cells, indicating its potential as a cancer treatment.
Scientists at The University of Tokyo's Institute of Industrial Science have developed a novel theory for describing nonlinear dissipative phenomena in a dual geometric space. This work enables the extension of thermodynamics to complex chemical reaction networks, including those involved in living organisms' metabolism and growth.
Researchers from The University of Tokyo created a geometric technique to characterize self-replication processes, shedding light on living systems' environmental conditions. This work aims to improve our understanding of biological reproduction and the theoretical limits governing chemistry and biology.
A recent study published in Construction and Building Materials has found that heat treatment can significantly improve the strength of recycled concrete, reducing its environmental impact. The researchers developed an energy-efficient means of improving recycled concrete outcomes through thermal treatment.
Researchers from the Institute of Industrial Science, The University of Tokyo, found that preordering significantly influences crystal growth and nucleation. Their study proposes modifications to address shortcomings in classical nucleation theory.
Researchers developed a new machine learning method called MotifBoost that can predict infections based on limited T-cell receptor data. By focusing on short sequences of amino acids in the receptors, the approach achieved more accurate results with smaller datasets, shedding light on the human immune system's recognition of germs.
Researchers at The University of Tokyo have created a new antibody-based method for detecting SARS-CoV-2 that does not require a blood sample. This innovative approach uses interstitial fluid from human skin to detect antibodies within 3 minutes.
Researchers from Japan have found that organic vapors can trigger the dissolution of molecular salts in a way similar to water vapor. This phenomenon, known as organic deliquescence, has potential applications for cleaning up indoor pollutants and can be used to remove volatile organic compounds (VOCs) from indoor environments.
Researchers from Japan developed a new model to assess mangrove forest productivity, which is influenced by environmental factors such as sea surface temperature and salinity. The model uses satellite data to estimate productivity and performed better than traditional terrestrial models.
Researchers found that climate change is shifting the risk of human-wildlife conflicts in Thailand, with northern areas more vulnerable to elephant-human interactions. The study suggests that as natural habitats become fragmented, conflicts may intensify in rural areas where agriculture relies heavily on land.
Scientists found that certain dynamical defects help explain the allowed vibrational modes inside amorphous solids, like glasses. These findings may lead to controlling the properties of amorphous materials.
Researchers from The University of Tokyo developed a novel machine-learning approach to predict local precipitation with high accuracy. By recognizing complex relationships in meteorological data, they created a bias correction method that produced accurate hourly estimates of precipitation.
A team of researchers used a new computer simulation to model the electrostatic self-organization of zwitterionic nanoparticles, which are useful for drug delivery. They found that including transient charge fluctuations greatly increased the accuracy, leading to the development of new self-assembling smart nanomaterials.
A team of researchers at The University of Tokyo has created a model that reveals the role of emergent elastic fields in chiral molecular and colloidal crystals. The findings provide a potential switch for developing new electro- and magneto-mechanical devices.
Scientists from the Institute of Industrial Science have developed a theoretical model for optimal search strategy in biological systems, which may help design new drones or nanobots. The model uses stochastic optimal control theory to analyze chemotaxis, a process of attraction to chemical gradients.
Researchers from The University of Tokyo developed a model to predict the occurrence of red snow events, which are associated with the duration of snow melt and the timing of new snowfall. The study found that snow algae blooms can speed up snow melt as they darken the surface.
A team of researchers from the Institute of Industrial Science, The University of Tokyo, used a mathematical model to examine the implications of intergenerational learning. They found that learning accelerated the evolutionary process, which may assist in designing more efficient hybrid algorithms.
Researchers studied electron transport through a single water molecule in a C60 cage, revealing multiple tunneling-induced excited states. The findings suggest the transition between ortho- and para-water occurs simultaneously within a minute.
The study found that certain grain boundaries in strontium titanate exhibit enhanced thermal expansion, leading to potential material failures. This discovery highlights the importance of grain boundaries in material properties and has implications for selecting suitable materials for various applications.
Researchers from The University of Tokyo Institute of Industrial Science used computer simulations to study the aging mechanism that can cause an amorphous glassy material to turn into a crystal. By removing tiny irregularities in local densities, they found that it prevents atomic avalanches that trigger ordered structure formation.
Researchers from The University of Tokyo Institute of Industrial Science used microscopy to examine surfactant onion layers, discovering they contain defects. Their findings are crucial for designing effective therapeutic carrier systems.
Researchers from The University of Tokyo Institute of Industrial Science have found that drones can be used as communication bases with underwater robotic devices (AUVs) for ocean surveys. UAVs offer high-speed observations, mobility, and resistance to ocean currents, making them suitable candidates for this application.
Researchers used machine learning to analyze core-loss spectroscopy data, revealing connections between spectral data and material properties. The study successfully predicted intensive and extensive material properties, enabling high-throughput development of new materials.
Infection with measles virus triggers the activation of both RNA and DNA virus immune responses. The virus affects mitochondrial growth and division, causing their fusion and release of mitochondrial DNA into the cytoplasm. This leads to the activation of the cGAS immune response, which is also triggered by DNA viruses.
A new study incorporated water vapor isotope compositions into a general circulation model to improve forecast accuracy by several percentage points. The Isotope-incorporated Global Spectral Model (IsoGSM) demonstrated improved modeling of air temperature and specific humidity.
Researchers from The University of Tokyo Institute of Industrial Science have identified the origin of a phenomenon that occurs when rubber materials under stress rapidly break. Their simplified step-loading model replicates the non-monotonic mechanical behavior observed in these materials, shedding light on the velocity jump phenomenon.
Researchers developed a machine learning model to predict bond characteristics, such as binding energy and Fermi energy, based on individual component parameters. The model achieved accurate predictions across various systems, offering potential benefits for material design and development in fields like catalysis and nano clusters.
Scientists developed a machine learning algorithm that uses artificial neural networks to accurately forecast cell size as it grows and divides. By recognizing patterns in the data, the computer can make more complex predictions than conventional methods, which rely on simplifying assumptions.
Scientists from Japan and China have discovered three previously unknown mechanisms of phase transition in soft materials, shedding light on the dynamics of solid-to-solid transformations. This research has diverse applications, including targeted drug delivery and development of new materials with tailored properties.
Researchers from The University of Tokyo Institute of Industrial Science have developed a new flood forecasting system that can predict extreme flooding events with a 32-hour lead time. This system is based on models of land surface and river routes, combined with meteorological data and statistical analysis.
A team of researchers created particles with an off-center core that can be tracked under a microscope to study rotational dynamics. Their findings show that neighboring spheres rotate coupled and move in opposite directions, like meshed gears, and that there is a relationship between local crystallinity and rotational diffusivity.
Integration of a mobility-enhanced field-effect transistor (FET) and a ferroelectric capacitor enables the creation of high-density, energy-efficient embedded memory directly on a microprocessor. This design significantly reduces signal travel distance, speeding up learning and inference processes in AI computing.
Researchers at The University of Tokyo have developed a new method to recycle discarded fruit and vegetable scraps into strong construction materials. The process uses vacuum-dried, pulverized food scraps, such as seaweed and cabbage leaves, and produces materials that are at least as strong as concrete.
The study reveals that thick and rough solid-liquid interfaces facilitate rapid crystal growth by breaking up disorder. Disordered states are inherently unstable mechanically, leading to a domino-like chain reaction of crystal growth.
Researchers at The University of Tokyo have developed a novel cell therapy using a lotus-root-shaped device to transplant human pancreatic beta-cells in the long term. The device, called LENCON, was shown to successfully control blood glucose levels for over 180 days in diabetic mice.
A research team from the University of Tokyo analyzed global flood risk modeling uncertainties, finding large uncertainties mainly associated with runoff data. The study identifies key areas for improvement in hydrological modeling to enhance future predictions of flood risk.
Researchers from The University of Tokyo have shown that the standard model biologists use to describe bacterial chemotaxis is mathematically equivalent to optimal dynamics. By using nonlinear filtering theory, they found that the system used by bacteria is indeed optimal for efficient sensing and adaptation in noisy environments.
Researchers at The University of Tokyo used artificial intelligence to show that T helpers in the adaptive immune system act like a neural network, optimizing responses to pathogens. The study may lead to improved vaccine development and stronger immune responses.