Articles tagged with Computational Science
Researchers measured dielectric properties of 11 polyimides to establish correlation between molecular structure and dielectric behavior. The study revealed that higher fluorine content resulted in lower dielectric constant values, enabling potential applications for 6G technologies.
Researchers developed a crystalline solid that can adsorb and release ammonia, making it easy to recover. The material's high density and ease of desorption make it a promising solution for efficient hydrogen storage.
A groundbreaking approach inspired by bio-inspired neuromorphic imaging and speckle correlography has unveiled a revolutionary technique for optical image encryption. This method leverages computational neuromorphic imaging to encrypt images into event-stream ciphertexts, significantly enhancing security and complexity.
A new study reveals that data on popular community science platforms is biased towards easily identifiable and attractive species. Researchers found that iNaturalist's reliance on photos creates a personal bias, underreporting certain species and overreporting others.
Researchers at USC developed a new method to accurately predict wildfire spread using satellite data and artificial intelligence. The model offers a potential breakthrough in wildfire management and emergency response, providing more precise and timely data for firefighters and evacuation teams battling wildfires.
Researchers at Argonne National Laboratory have developed biodegradable and recyclable luminescent polymers that can break down under heat or mild acid. The material showed a tenfold increase in light-emitting efficiency, making it suitable for applications such as displays and medical imaging.
Researchers developed a method to assess the reliability of foundation models, enabling users to choose the best model for their task without testing it on real-world data. The approach measures consensus among multiple models and aligns representations to compare consistency.
Researchers created a new cortical surface template called 'OpenNeuro Average' that provides greater accuracy and efficiency in analyzing neuroimaging data. The template is based on the geometric shape of the brain and can be used for studies on cognition, clinical neuroscience, autism, and neurodegenerative diseases.
A recent study predicts that lakes worldwide will experience unprecedented surface and subsurface warming, leading to severe disruptions in ecosystems. Tropical lakes are expected to be the first to emerge from natural temperature bounds, while high-latitude lakes may shield their subsurface layers from surface warming.
Scientists from Trinity College Dublin created a computer program that visualizes molecular structure in the style of Piet Mondrian. The program uses blocks of color to represent symmetry and shape, making it easier to understand complex molecular interactions.
Scientists developed customized AI tools, including ChatGPT, to provide accurate responses on digital pathology. The tools help pathologists without extensive coding experience analyze tissue samples, bridging the gap between pathology and digital pathology skills.
A deep metagenomic sequencing study in Berlin wastewater reveals common viruses like RSV and flu, seasonal visitors like asparagus-infecting viruses, and astroviruses with potential for gastrointestinal tract infections. The analysis also identifies novel enzymes with biotechnological potential, expanding our knowledge of viral diversity.
A team of researchers developed EMOKINE software to measure the objective kinematic features of movements that express emotions. The software provides movement parameters from data sets at the touch of a button, allowing scientists to analyze emotional expression and intentions.
A new imaging technique allows scientists to visualize the Earth's rocky interior using GPS data, revealing details about the planet's crust and mantle. This method has the potential to improve earthquake predictions by combining it with other techniques.
A new model developed by Flatiron Institute researchers proposes that individual neurons exert more control over their surroundings, which could be replicated in artificial neural networks. This updated model treats neurons as tiny 'controllers' and may lead to better AI performance and efficiency.
A platform called Open-ST enables scientists to reconstruct gene expression in cells within a tissue in three dimensions, capturing molecular and (sub)cellular structures. The platform was used to study cell types at subcellular resolution in tissues from mice brains, tumor tissue, and healthy lymph nodes, providing insights into cance...
A new study found that animals use a wide range of strategies to accomplish tasks, many of which are just as effective as the optimal solution but require less brain power. The research provides a theoretical framework for understanding these 'good enough' strategies and their potential applications in animal behavior.
Researchers have discovered that gallium's bonds disappear at melting point but reappear at higher temperatures, leading to a new explanation for its low melting point. This breakthrough has important implications for advances in nanotechnology and materials science.
A new system named SQUID, a computational tool created by Cold Spring Harbor Laboratory scientists, helps interpret how AI models analyze the genome. It reduces background noise and leads to more accurate predictions about genetic mutations.
Researchers create special language Phenoscript to describe traits in a way that both humans and computers can understand. This allows for large-scale studies of organismal traits, boosting the emerging field of phenomics. The new language is being tested and aims to integrate semantic species descriptions with nanopublications.
Researchers from MIT develop a new technique called Natural Language Embedded Programs (NLEPs) to enable large language models to solve numerical, analytical, and language-based tasks. NLEPs achieved greater than 90 percent accuracy on symbolic reasoning tasks and showed 30 percent greater accuracy than task-specific prompting methods.
Researchers developed a quantum annealing approach to accelerate data assimilation, significantly reducing computational cost. The method achieves comparable accuracy to conventional approaches but in a fraction of the time.
A new study by a global consortium provides insight into how tumors evolve, shedding light on the intricate processes underlying cancer evolution. The findings define optimal algorithms to analyze tumor evolution, enhancing diagnostic accuracy and treatment planning.
Researchers at NCCR MARVEL have discovered a chain of copper and carbon atoms that forms the thinnest metallic nanowire stable at 0K. CuC2 has promising properties for flexible electronics, including its ability to be bent without losing its metallic behavior.
A new method developed by researchers at Florida Atlantic University and the Smithsonian Environmental Research Center improves the accuracy of acoustic tracking in marine animals. The method uses a movement model to reconstruct animal tracks, even in regions with uneven receiver coverage.
A new system developed by Boyce Thompson Institute makes affordable, mobile and high-throughput phenotyping tools accessible worldwide. This allows researchers to conduct data-driven studies on plant growth and responses over time, accelerating breeding of more resilient crop varieties.
Researchers at La Jolla Institute for Immunology developed a computational method to link gene activity to molecular marks on DNA, potentially aiding in the detection of solid tumors and more accurate cancer diagnoses. This new approach utilizes machine learning tools to identify connections between genes and enhancers in the genome.
Portland State University has secured a nearly $1 million grant from the National Science Foundation's Campus Cyberinfrastructure program to establish the Oregon Regional Computing Accelerator (Orca) cluster. The cluster will provide free-of-cost computing resources and cyberinfrastructure to colleges in rural, regional, and minority-s...
A novel approach to training AI systems uses information about spatial position to identify objects and navigate surroundings, inspired by children's visual development. The method improves contrastive learning models' effectiveness by incorporating simulated spatial context information, outperforming base models in various tasks.
Cleveland Clinic and IBM researchers develop a hybrid framework combining quantum and classical computing methods for protein structure prediction. This approach overcomes limitations of current classical methods and demonstrates improved accuracy in predicting protein structures.
The study enhances shape-from-shading technique to create detailed models of lunar terrain with higher resolutions and faster production speeds. Researchers use advanced computer algorithms to automate the process, resulting in more accurate maps that show subtle features and variations of lunar surface terrain.
Researchers at Lancaster University and Radboud University Nijmegen have discovered a novel pathway to modulate and amplify spin waves at the nanoscale, paving the way for dissipation-free quantum information technologies. The study's findings could lead to the development of fast and energy-efficient computing devices.
Elastic turbulence, a chaotic fluid motion in non-Newtonian fluids, exhibits universal power-law decay of energy and intermittent behavior. This study reveals its unexpected similarity to classical Newtonian turbulence, paving the way for developing a complete mathematical theory and predicting flow patterns.
Researchers from the University of Bath have created a deformable screen called DeformIO, which can be deformed through finger pressure. The surface becomes softer or stiffer in direct response to force applied, allowing users to interact with digital objects in a more tactile way.
Researchers in Portugal have developed an innovative open-source platform called DL4MicEverywhere, providing life scientists with easy access to advanced artificial intelligence for the analysis of microscopy images. The platform enables researchers to easily train and use deep learning models on their own data, regardless of their com...
A novel multifunctional catalyst has been developed to convert methane into valuable hydrocarbons, reducing greenhouse gas emissions and energy consumption. The catalyst's spatial distribution of Cu and acid sites determines the final products, with uniform distribution leading to stable and efficient methanol production.
Salk scientists develop new method to quantify synaptic features, revealing high precision of plasticity and up to 10 times more storage capacity. The technique uses information theory to analyze synapse pairs from a rat hippocampus, offering a scalable approach for studying brain function.
Researchers created a digital twin model that predicts and controls complex systems, achieving higher accuracy than traditional methods. The algorithm is compact, energy-efficient, and easy to implement, making it suitable for self-driving vehicles and other dynamic systems.
A research team from USTC proposed a novel design approach for GRN generators, accommodating arbitrary σ values and output ranges. They introduced a scaling index to adjust relevant values differently based on varying σ values, resulting in a stable error curve and facilitating real-time configuration of σ values.
A new AI method developed by Swedish researchers can identify toxic substances based on their chemical structure, potentially replacing animal testing. The method has been shown to be more accurate and broadly applicable than existing computational tools, offering a promising alternative for environmental research and authorities.
Researchers designed a new AI model that accurately predicts treatment outcomes for stroke prevention in people with heart disease. The model, called CURE, was trained on a massive dataset of de-identified patient information and fine-tuned to focus on specific health conditions and treatments.
A team of astronomers and citizen scientists has discovered a planet in the habitable zone of an unusual star system, including two stars and potentially another exoplanet. The newly discovered planet, TOI 4633 c, boasts the second-longest orbit of any planet discovered with TESS data.
A mathematical model developed by Alexandre Solon and Eric Bertin describes the movement of particles in situations similar to cars on a road or bacteria attracted to a nutrient source. The model identifies conditions that favor traffic jams, including high vehicle density and driver inertia.
A team of researchers from Shibaura Institute of Technology developed a self-folding origami gripper that can be mounted onto drones, enabling them to grasp and hold objects. The gripper weighs only 5 grams but demonstrates grasping force equivalent to holding a 130-gram object.
Using AI software called SLEAP, scientists at Salk Institute are designing climate-saving plants with optimized root systems that can store more carbon. This approach enables researchers to analyze plant features and connect desirable traits to targetable genes, accelerating the development of carbon-capturing plants.
Researchers have achieved record speeds of up to 900 m/s by moving magnetic skyrmions using electrical currents. This breakthrough offers new prospects for developing higher-performance and less energy-intensive computing devices.
Egg cells generate internal fluid flows to transport nutrients, but how these flows arise has been a mystery. Researchers used computational models and experiments to understand the mechanics of twister-like fluid flows, revealing their origin from microtubules and molecular motors.
A new machine-learning approach outperforms human testers in generating diverse prompts that trigger a wider range of undesirable responses from chatbots. The technique provides a faster and more effective way to ensure the safety of large language models.
A new statistical-modeling workflow can quickly identify molecular structures of products formed by chemical reactions, accelerating drug discovery and synthetic chemistry. The workflow also enables the analysis of unpurified reaction mixtures, reducing time spent on purification and characterization.
Belgian scientists developed AI models to predict beer ratings and aroma compounds, improving beer quality. The study analyzed hundreds of beers and used machine learning to connect chemical concentrations with appreciation scores.
Researchers from Massey University and Michigan State University discuss the limit of the periodic table with recent advances in superheavy element research. They aim to uncover properties of atoms and nuclei beyond the current atomic number and mass.
A novel mixed light field technique was developed to achieve immersive augmented reality experiences in bright environments, preserving natural shadows and contrast. The approach utilizes ray-controlled lighting and a binary search algorithm to remove ambient lighting from PM targets.
Researchers are developing a satellite-based quantum light source for secure communication, leveraging the laws of physics to encode and transmit data. The technology has the potential to extend quantum cryptography over long distances, enabling secure communications between cities or continents.
Researchers aim to address LVAD shortcomings, reducing blood damage and clotting risk through innovative designs and coatings. A novel flexible stented blood inlet and slippery hydrophilic coatings will be used to prevent flow stasis and clot formation.
New research combines images with computer-enabled analysis to tackle biological questions globally. Imageomics aims to improve image classification and analysis using machine learning and computer vision, enabling faster scientific discoveries.
Researchers at Tohoku University have developed a high-performance spin wave reservoir computing model that utilizes spintronics technology. The breakthrough could lead to energy-efficient, nanoscale computing with unparalleled computational power.
Scientists create detailed models of the terrestrial water cycle using high-resolution satellite data to predict floods, droughts, and other extreme events. The Digital Twin Earth Hydrology Platform allows for interactive simulations and visualizations to enhance flood and landslide prediction and optimize water resource management.
Two new studies explore the link between attention and eye movements, showing that attention can be lost when objects change suddenly before gaze falls on them. Researchers also found distinct neuronal signatures in primate visual cortex for attention and eye movement signals.
Researchers at UCSF used clinical data and a precision medicine approach to identify early risk factors for Alzheimer’s disease, predicting its onset with 72% accuracy. High cholesterol, osteoporosis, and erectile dysfunction were found to be predictive factors in both men and women.
Scientists used Delta's GPU-based architecture to study the life cycle of the Hepatitis B virus, revealing how it binds human proteins called importins. The research provides a platform for developing inhibitors that could block this interaction and prevent the virus from accessing the nucleus.