Articles tagged with Computer Modeling
A new study reveals that just five US universities have trained 1-in-8 tenure-track faculty members, highlighting the dominance of academic pedigree in academia. The research also shows that academics from less prestigious schools are more likely to leave the field and face limited job opportunities at elite institutions.
Researchers developed a machine learning algorithm that can generate a continuous 3D model of cells from a partial set of 2D images. The neural field network learns a mapping from spatial coordinates to physical quantities, allowing for smooth zooming and no pixelation.
University of Rochester researchers have quantified the energy of ocean currents larger than 1,000 kilometers using a coarse-graining technique. The most energetic current is the Antarctic Circumpolar Current, spanning 9,000 kilometers.
A new study developed an analytical model that can predict Lyme disease bacteria distribution and abundance on the landscape. The research found that climate and habitat disturbance were key factors in determining infectivity rates.
Researchers developed a unified machine learning model that analyzes data from patients with and without treatment, predicting depression outcomes better than separate models. The approach enables personalized medicine by designing treatment plans specific to each patient's needs.
The AI model, trained with end-to-end and multi-task manners, achieves best drivability in a standard simulation environment, outperforming recent models. It extracts useful information from a single RGBD camera and uses sensor fusion techniques to enhance performance.
Researchers at Drexel University have developed a computer model that uses machine learning algorithms to analyze the genetic sequence of the COVID-19 virus and predict the severity of new variants. The model provides an early warning system for public health officials, allowing them to prepare accordingly.
Researchers at Chalmers University of Technology developed a computer model to predict enzyme efficiency. This helps find efficient cell factories for producing biotech products like biofuels and medicines, and studies difficult diseases.
Scientists modeled how different coronavirus shapes affect rotational diffusivity, impacting transmission and infective success. The study found that more elongated shapes result in slower rotation rates, potentially improving attachment to cells.
A researcher will combine ARM data and fine-scale modeling datasets to understand marine boundary layer aerosol-cloud-precipitation interactions. The goal is to improve climate models' portrayal of cloud-processing of aerosols and address uncertainty in global climate models.
Researchers at the University of Oldenburg and Fraunhofer IWES collaborate on a new project to develop more accurate wind flow simulations using artificial intelligence. The goal is to reduce computing times and enhance precision, ultimately accelerating innovation in wind turbine design.
Researchers at MIT developed an AI model that can detect Parkinson's disease from breathing patterns, using a neural network to assess the presence and severity of the condition. The device is non-invasive and can be used in patients' homes without any bodily contact.
Researchers found that non-growing season nitrous oxide emissions in the Midwest can account for up to 60% of annual emissions. Environmental factors such as precipitation and temperature drive these emissions, with more intensive freezing causing increased emissions in the southeastern Midwest.
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.
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.
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.
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 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.
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.
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.