Articles tagged with Computer Modeling
The Psi-k conference is a premier event for researchers in computational materials science, attracting experts from 50 countries. The conference features 38 symposia, 115 invited talks, and 250 contributed talks on topics ranging from materials discovery to emerging computing.
Researchers at Hokkaido University propose two novel hypotheses to address the 'two-fold cost of sex' in sexual reproduction. The first hypothesis suggests that meiosis eliminates harmful mutations through a process known as the 'seesaw effect.' The second hypothesis proposes that anisogamy evolved through 'inflated isogamy,' where lar...
NTU Singapore has launched the Algorand Centre of Excellence at NTU (ACE@NTU), a new research and education centre focused on developing and advancing blockchain technologies. The centre aims to become the nexus for blockchain education and research in Singapore and the region.
Scientists at Giessen University used high-performance computing to understand the optical response of cluster glass, a material that generates bright, clear white light. The study verified the experiment through simulation and showed the link between the observed properties and molecular structure.
Undergraduate students at Ohio University applied kinetic techniques learned in physical chemistry to analyze the COVID-19 spread data from the CDC. They developed a model that considers the effect of vaccination and made predictions for the following months, correctly predicting the surge in cases in Ohio.
Scientists have developed a new model incorporating the day/night cycle into a global ocean biogeochemistry model to investigate its effects on phytoplankton. The study found that diel light cycles significantly impact phytoplankton competition, particularly at lower latitudes.
Researchers at Cedars-Sinai created complex computer models of individual brain cells using artificial intelligence. These models capture the electrical signals that neurons fire to communicate with each other, allowing researchers to replicate brain activity at the single-cell level.
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.
A computational modeling study explores the relationship between desire for more and happiness, finding a potential bias in our pursuit of pleasure. The research suggests that this bias can lead to unhappiness, highlighting the importance of balance and moderation.
Researchers at Max Planck Institute for Psycholinguistics found that our brain is a prediction machine continuously making predictions on multiple levels. They analyzed brain activity while people listened to Hemingway or Sherlock Holmes stories and text, finding the brain response was stronger when words were unexpected in context.
Researchers at Stanford University have developed Unified Many-Worlds Browsing, a new approach to refine the search process for physics-based animations. By allowing animators to create queries to narrow down options, this framework can potentially reduce thousands of possible outcomes to a handful that are interesting to the user.
A team of researchers from Boston University and EcoHealth Alliance will develop models to predict disease emergence and spread. They aim to identify location hotspots for pathogen emergence and determine the most effective pandemic mitigation strategies using data from COVID-19, H1N1 flu, and Ebola Virus Disease.
A team of scientists developed a computational model that explains Italy's town distribution using only a small set of mathematical equations and a map of the landscape. The model simulates how population and road networks interact, demonstrating that landscape alone is insufficient to explain population distribution.
A UBC research team created a computer modelling program to predict coral reef impact and restoration plans. They found that more diverse communities are most resilient, with species having unique traits contributing to habitat quality.
KAUST researchers have developed a new method to simulate viscous liquids up to 15 times faster than the current state of the art. This breakthrough enables faster simulations for industrial processes, medical devices, computer graphics, and visual simulations.
Using the Stampede2 supercomputer, researchers have developed a deep learning model that predicts the properties of over 370,000 high-entropy alloy compositions. The study also applied association rule mining to discover design rules for high-entropy alloy development and proposed several compositions for experimentalists to synthesize.
Researchers have discovered that resistivity can cause instabilities in plasma edge, making it more stable when included in models. The study aims to design systems for future fusion facilities with improved plasma stability.
Researchers developed a methodology to attribute coastal glacier retreat to human-caused climate change, revealing that even modest global warming causes most glaciers to melt or retreat. The approach simulates the behavior of real ice sheets like Greenland's, helping predict major ice loss and informing decision-making for policymakers.
A team of experts is working on new software tools and industrial guides to improve concrete mixes, structural designs, and construction processes. The project aims to mitigate the risk of construction defects related to fresh concrete, reducing costs and delays.
Researchers at Boston University developed an AI-powered computational model that can detect cognitive impairment from audio recordings of neuropsychological tests. The model was trained on over 1,000 individuals and accurately distinguished between healthy individuals and those with dementia.
Researchers from North Carolina State University have developed a new approach to federated learning that allows them to develop accurate AI models more quickly and accurately. By reformating updates sent to the centralized server, they can resolve the heterogeneity problem in data, improving model performance.
Researchers developed a novel convolutional neural network for facial expression recognition, outperforming conventional models while being computationally less expensive. The new model achieved an accuracy of 72.4% using only 58,000 parameters.
A novel quantum simulation method clarifies the correlated properties of complex material 1T-TaS2. The study reveals that the insulating behavior stems from a complex interplay between bonding-antibonding splittings and electronic correlation.
Researchers analyzed the Hunga Tonga-Hunga Ha'apai volcano's eruption to understand its effects on atmospheric waves. The blast provided an unprecedented view of atmospheric waves, allowing scientists to better predict the weather and climate.
A UTSA professor will use a five-year $550,000 grant to study natural language processing and develop NLP models tailored to specific population groups. The goal is to improve the accuracy and relevance of these models in everyday applications.
Researchers created an atomic-level computational model of the COVID-19 spike protein, finding that human cell modifications increase its flexibility and mobility. The study suggests that these modifications can enhance virus infectivity and immune avoidance.
Pitt and Princeton engineers develop a system that converts chemical energy into mechanical action, allowing two-dimensional polymer sheets to rise and rotate in spiral helices without external power. The self-assembly process creates a complex, three-dimensional structure resembling twisted yarn being formed by a rotating spindle.
A new computer model has been developed to rapidly scan cancer genomes and identify harmful driver mutations that contribute to tumor growth. The model, trained on genomic data from various types of cancer, found additional mutations in 5-10% of patients that could help doctors identify more effective treatment options.
A new approach based on deep learning AI detects weak gravitational signals, or PEGS, generated by large-mass motion in megaquakes. This allows for real-time tracking of earthquake growth after a magnitude 8 event.
Researchers developed a mathematical model that brings together physics and chemistry involved in dendrite formation, suggesting swapping new electrolytes with certain properties could slow or stop dendrite growth. The study aims to guide the design of lithium-metal batteries with longer life span.
Researchers found that wetter pre-growing seasons reduced soil nitrogen through leaching, but applying more fertilizer can mitigate this effect. The model also showed that cold pre-growing season temperatures limited early growth in ways that affected yield potential, making extra fertilizer less effective.
A team from KAUST has developed a low-cost system for imaging plant growth dynamics noninvasively and at high throughput. The Mutiple XL ab system combines computer vision and pattern recognition technologies with machine learning to analyze and quantify root growth dynamics.
Researchers from Johannes Gutenberg University Mainz and partners will continue developing fundamental soft matter simulation methods, improving techniques and applying them to real-world problems. The project aims to establish routine use of multiscale techniques for simulating soft material properties.
Researchers developed a machine learning model to predict NAFLD development based on gut microbiome data, showing 90% of subjects who developed the disease had subtle differences in their samples. The model combines easily measurable information from blood and microbiome data with high accuracy.
Researchers at PPPL have discovered that adding tungsten to plasma fuel pellets improves the compression of fuel, increasing fusion yield. The study uses krypton gas to measure X-rays emitted by the pellets, providing new insights into the fusion process.
Researchers developed a computational model to determine optimal places for electric vehicle charging facilities and powerful stations without straining the local power grid. The model considers travel flow, user demand, and regional power infrastructure needs.
Researchers have developed a rigorous computer simulation technique to optimize LNG tank design, reducing construction costs while improving safety against catastrophic failures. The new model can be used to mitigate environmental and economic consequences of failure, enabling Australia to store more energy at the right time.
A team of researchers from Waseda University developed a novel solution to efficiently solve complex optimization problems using Ising machines. Their hybrid algorithm reduces residual energy and reaches more optimal results in shorter time, increasing the machine's applicability across industries and sustainability practices.
A new machine learning tool assesses extinction risk for reptiles with limited data, revealing a higher number of threatened species than previously estimated. The study highlights the need for increased conservation efforts in regions such as Australia, Madagascar, and the Amazon basin.
Researchers simulated COVID-19 transmission on a university campus, finding that universal mask usage and high vaccination rates can curb new infections. The study suggests that at least 93% of students should be vaccinated during highly transmissible variant outbreaks.
Researchers have successfully processed sequences with a large neural network while consuming significantly less energy on neuromorphic hardware. This breakthrough showcases the potential of neuromorphic technology to improve the energy efficiency of AI workloads.
A new project led by University of Illinois professor Jingrui He seeks to detect and predict insider threats in large organizations. The team will use multimodal data to identify outliers and rare category types of insider threats and propose dynamic update techniques.
Parents generally accept AI tools for managing children with respiratory illnesses in the ED, but younger and non-Hispanic Black parents have greater concerns. The study highlights the importance of involving diverse stakeholders in developing AI systems for pediatric healthcare.
A team of University of Hawaii researchers found that the number of hours of darkness during the lunar cycle triggers mature Hawaiian box jellyfish to swim to shore to spawn. The study also revealed that jellies are likely to come from the lee of Diamond Head Crater, where they benefit from shelter and food.
Researchers at Princeton and Northwestern universities developed a computational model of the pyrenoid, identifying key features needed for enhanced carbon fixation in plants. The study suggests that engineering a pyrenoid-like ability could improve crop growth rates and mitigate food insecurity.
Researchers from Korea Maritime and Ocean University have developed a way to synthesize high-performance functionally graded materials with minimized defects. By controlling the mixing gradient of component materials, they improved mechanical properties and eliminated interfacial cracks.
A research team developed a model that mimics human judgment to distinguish between reflective and transparent materials. The model outperformed humans in accuracy but struggled to identify image clues.
Researchers at Duke University have developed a machine learning algorithm that incorporates known physics into neural networks, allowing for new insights into material properties and more efficient predictions. The approach helps the algorithm attain transparency and accuracy, even with limited training data.
The European drought event from 2018 to 2020 was the most intense in over 250 years, affecting approximately one third of the land area. The drought's total duration was unusually long, starting in April 2018 and ending in December 2020.
A team of University of Illinois researchers, led by Charles Gammie, has captured the first direct visual evidence of a supermassive black hole at the center of the Milky Way. The image reveals a dark central region surrounded by a bright ringlike structure, providing valuable clues about the workings of such giants.
Researchers developed an algorithm to identify users' basic needs from their Instagram posts, images, and captions. The study analyzed 86 profiles in Spanish and Persian, achieving promising accuracy and complementary information between visual and textual cues.
Xie aims to develop algorithms that reflect human domain knowledge and reasoning patterns, increasing trust in machine learning models. He also explores algorithmic fairness, considering multiple perspectives on what's considered fair, to address concerns about biased decision-making.
A study by Cornell University researchers found that online retail images of models have statistically lighter skin tones than videos of the same product and model. The discrepancy was also linked to 'tokenism,' where one model with darker skin is used to represent a range of diversity.
The study found that even if Earth's climate stopped warming, it would be difficult to rebuild the ice shelf once it has fallen apart. The researchers suggest that the ice shelf may not recover unless the future climate cools considerably. This has significant implications for sea-level rise and the stability of polar ice sheets.
Researchers aim to overcome physical limitations of telecommunications by creating immediate response regardless of distance. The eTouch project leverages Model-Mediated Teleoperation and Edge Computing to enable haptic feedback perception without noticeable delay.
A team of international researchers created digital models of disease interactions to identify key proteins and signaling cascades in seasonal allergies. They found that inhibiting PDGF-BB protein was more effective than existing treatments, suggesting a potential breakthrough in personalized medicine.
A team of physicists and chemists at the University of Surrey used computer modeling to show that quantum mechanics can cause errors in DNA replication, leading to mutations. The researchers found that protons can tunnel through energy barriers, causing mistakes in the pairing of DNA bases.
A new method reduces computational complexity of traffic models, making them operate more efficiently. The modified algorithm breaks down complex forecasting questions into smaller problems that can be solved in parallel, significantly reducing run time. This approach also allows for a good enough solution within an error bar, rather t...
MIT researchers develop ExSum, a framework to formalize explanations of machine-learning models into quantifiable rules. This allows for testing assumptions about model behavior and reveals unexpected insights, such as negative words having sharper contributions to model decisions.
A new model, GCM, uses graph neural networks to capture user behavior interactions and factorization machine to model feature interactions, demonstrating effectiveness on public datasets. The research advances context-aware recommender systems by leveraging graph learning for strong user and item representations.