Articles tagged with Computer Modeling
Researchers found that strong supernatural belief systems can effectively reduce environmental harm by encouraging restraint and promoting sustainable relationships between humans and nature. The study's model showed that supernatural punishments are effective when the fear of punishment outweighs short-term gains and is not too extreme.
Researchers at Washington State University found that early planting to avoid heat damage may actually hinder spring wheat productivity due to other growth issues. The study used computer modeling to show that moving crop plantings earlier in the season can expose crops to elevated heat or cold stress in later growth stages.
Researchers at MIT have found a hidden atomic order in metals that changes their properties, including mechanical strength and heat capacity. The discovery reveals a new physical phenomenon explaining the persistent patterns and provides a simple model to predict chemical patterns in metals.
Professor Dan M. Frangopol, Lehigh University's inaugural Fazlur R. Khan Endowed Chair, was recognized for his work on life-cycle civil engineering at the 21st International Probabilistic Workshop in Germany. He is widely credited with establishing this field and has authored over 500 peer-reviewed journal articles.
Researchers developed ECODATA, an open-source tool for exploring animal movements in the wild by creating animations that help ecologists study animal movement, climate change and conservation efforts. The software combines direct wildlife location observations with complex remote sensing data to create customizable maps.
Researchers have developed an AI model that creates a detailed map of the mouse brain featuring 1,300 regions/subregions. The CellTransformer model uses spatial transcriptomics data to identify previously uncharted subregions of the brain, opening new avenues for neuroscience exploration.
Scientists at MIT developed a method to predict how plasma in a tokamak will behave during rampdown, achieving high accuracy with limited data. This new model could significantly improve the safety and reliability of future fusion power plants.
A team of researchers from Worcester Polytechnic Institute has developed a new approach to producing hydrogen using plasma technology and metal alloys. The method reduces energy consumption and carbon emissions compared to traditional methods, making it more environmentally friendly and potentially affordable.
A solar sail probe could provide a new vantage point for detecting space tornadoes, also known as flux ropes, which can disrupt Earth's magnetic field. These smaller features are too small for typical simulations, but a solar sail probe can help scientists see them and predict potential hazards.
Despite no decrease in usage, Wikipedia faces challenges from AI data scrapers and Large Language Models. The platform is struggling to allocate resources due to the influx of AI training data, threatening its current structure.
New supercomputer simulations from the Texas Advanced Computing Center have found improved estimates of ice mass Enceladus is losing to space. The findings help with understanding and future robotic exploration of what's below the surface of the icy moon, which might harbor life.
The book offers novel human-centered AI techniques to address social intelligence challenges, including multimodal approaches and explainable AI designs. Researchers can integrate artificial and human intelligence to tackle complex social challenges.
A research team has discovered the structural origins of mechanical softness in amorphous materials like glass, attributing it to hierarchical ring structures that coexist with medium-range order and local disorder. This finding will accelerate the design of flexible and strong amorphous solids.
The partnership aims to provide cutting-edge professional development to public sector and critical infrastructure leaders across Canada, equipped with strategic skills to outthink and outmaneuver threats. The initiative strengthens Canada's cyber resilience by enhancing national cyber resilience.
The CERCat consortium aims to improve the field of catastrophe modeling by uniting academic rigor with practical expertise, driving real-world solutions that enhance disaster resilience. The consortium is prioritizing projects on multihazard fragility curves, wildfire fragility curves, and AI-powered post-disaster damage assessment.
University of Michigan researchers have made significant progress in developing a more accurate simulation approach for density functional theory, a widely used method in fundamental chemistry and materials science studies. The new approach has improved the calculation of exchange-correlation functionals, which describe how electrons i...
Researchers used precise neural activity measurements from epilepsy patients to study how brain processes speech. The findings suggest the auditory cortex operates on a fixed, internal timescale independent of speech structures, providing a consistently timed stream of information.
A deep learning model achieved up to 98% accuracy in distinguishing autistic from neurotypical participants, providing clear insights into brain regions most influential to its decisions. The model could benefit autistic people and clinicians by offering accurate and explainable results to inform assessment and support.
Researchers developed a fully automated finite element analysis pipeline to transform spine diagnostics and personalized treatment planning. The new approach enables rapid, patient-specific simulations that support preoperative planning, spinal implant optimization, and early detection of degenerative spine conditions.
Researchers will generate maps showing the intensity and extent of an earthquake, helping to identify regions with a higher probability of infrastructure damage. The simulation will recreate a hypothetical scenario of an 8.1 magnitude earthquake in Mexico's Michoacán state.
A new AI model can estimate the long-term risk of over 1,000 diseases and forecast human health changes over a decade. The model was trained on anonymised patient data from 400,000 participants and tested on data from 1.9 million patients in the Danish National Patient Registry.
Five new professors join Goethe University's C3S, focusing on biodiversity modeling, socioeconomic drivers of climate change, data surveillance, critical computational literacy, and the normative constitution of computation. Hagen, Linsenmeier, Akbari, Verständig, and Müller-Mall bring fresh momentum to the center.
The Leopoldina Annual Assembly will focus on the progress of artificial intelligence in various fields, including medicine, image creation, medical diagnoses, and decision-making processes. Experts from natural and social sciences will discuss the effects of AI on society, ethics, and democracy.
A team of scientists has estimated that an average cell line acquires and retains roughly 13 percent of its genes every million years through lateral gene transfer. This process enables microbes to adapt to new environments and access essential nutrients. The study provides the first quantitative analysis of gene transfer rates across ...
Tayfun Tezduyar's space-time computational flow analysis enables accurate modeling of complex systems, from designing parachutes for astronauts to simulating blood flow through heart valves. The approach provides high-fidelity representations in both space and time, allowing for more realistic solutions.
Researchers have developed a new method to accurately measure the acidity of PFAS, finding some to be thousands of times more acidic than previously thought. This breakthrough could help track the persistence and spread of these toxic chemicals in the environment.
Researchers at Saarland University discovered that molecular dipoles in ice and shoe soles interact to create a disordered, amorphous structure on the ice surface. This interaction causes the ice to become slippery, leading to slips and falls, rather than pressure or friction.
A study by BSC-CNS analyzed molecular data from over 4,000 patients and 45 diseases using a newly developed computational method. The results show that 64% of medically known connections are related by similarities in gene expression, providing clues about the biological mechanisms linking them.
Florida Atlantic University's FAU Engineering has received a $827,533 USD grant from the USDA to develop an advanced edge/fog computing-based framework called FogAg. This project aims to revolutionize precision agriculture by enabling real-time sensing and analysis of crop growth and yield.
The study reveals that when physical constraints are removed, human mobility follows a power-law pattern, decreasing steadily with distance. The researchers found this pattern across five orders of magnitude, from 10 meters to hundreds of kilometers.
Researchers at Tohoku University have developed an AI-built materials map that combines experimental data with computational predictions to identify promising materials for thermoelectric waste-heat recovery. The map enables faster development timelines and reduces trial-and-error, accelerating innovation in energy-related technologies.
Simpler, physics-based models can generate more accurate predictions than state-of-the-art deep-learning models for certain climate scenarios. However, simple models are more accurate when estimating regional surface temperatures, while deep-learning approaches excel at local rainfall estimation.
The inaugural AI in Agriculture Symposium will bring together experts from academia and industry to discuss the latest research and real-world applications for agriculture. The symposium will feature a roundtable discussion on multidisciplinary collaborations in agriculture.
A new computational framework has been developed to optimize cellular self-organization, allowing scientists to understand and control how cells grow and interact. The framework uses machine learning tools to extract rules that guide cell behavior, enabling the creation of artificial organs and potential treatments for cancer.
A new AI-assisted model combines MRI, biochemical, and clinical information to predict worsening knee osteoarthritis with high accuracy. The model showed improved accuracy in predicting worsening pain and joint space narrowing, suggesting its potential to enhance care.
The new model can predict how well any given molecule will dissolve in an organic solvent, helping chemists choose the right solvent for reactions. The researchers trained two models on a comprehensive dataset and found that their predictions were two to three times more accurate than the previous best model.
Research at the University of Rochester Medical Center and University College Dublin has revealed key insights into multisensory integration in the brain. The study found that when changes occur in both visual and auditory inputs, auditory and visual decision processes unfold in parallel but come together in the motor system.
Researchers from FAU and the Sensing Institute created a detailed computer model of the mouse brain's vasculature, simulating how brain blood vessels respond to hemodynamics and vasodynamics. The model shows that transitional vessels play a critical role in regulating flow and protecting the brain during increased activity.
PhyNetPy aims to create a unified framework for phylogenetic networks, enabling widespread use of complex models of evolutionary history. The project seeks to bridge the gap between phylogenetics and population genetics communities.
Andreas Papaefstathiou's research will help elevate the study of particle physics in nuclear collisions at Kennesaw State University. The grant will also contribute to the development of a new particle collider in the US, strengthening partnerships between universities.
New research finds that wildfire smoke substantially increases ozone concentrations, even in remote areas with few human emission sources. The study suggests that removing anthropogenic NOx emissions would not eliminate ozone problems, as fires can still produce ozone.
A large-scale modeling study led by MIT researchers reveals that dynamically adjusting vehicle speeds can cut annual city-wide intersection carbon emissions by 11-22%. Implementing eco-driving measures could also result in a 25-50% reduction in CO2 emissions if only 10% of vehicles adopt the technology.
Researchers are developing an AI imaging approach to distinguish between tumor progression and treatment effects in glioblastoma patients, promising to improve care and outcomes. The AI approach has already shown accuracy rates of up to 74% in initial testing.
Researchers found that bat boxes can reach lethal temperatures if not designed properly. Simulations showed that adding insulation, light colors, and thoughtful orientations can make the boxes safer. The study's code is publicly available, allowing conservationists to tailor their solutions to specific climates.
Researchers developed a customized parametric insurance system using Probabilistic Tsunami Risk Assessment (PTRA) to quantify tsunami losses and payouts. The proposed framework reduces overpayment by 60.9% while maintaining risk reduction, potentially saving lives and money.
A research team developed an innovative unsupervised model for industrial anomaly detection using paired well-lit and low-light images. The model leverages feature maps, Low-pass Feature Enhancement, and Illumination-aware Feature Enhancement to detect anomalies while remaining lightweight and memory-efficient.
This book provides a beginner-friendly resource on the impact and evolution of decentralized networks, highlighting their applications in healthcare, supply chains, agriculture, climate monitoring, and education. The authors emphasize sustainability, data security, and ethical tech adoption.
Researchers create a technique to model organic objects, creating photorealistic 3D images of complex shapes for autonomous sorting. This method improves robotic systems that sort microscopic marine fossils used in climate research.
The team aims to deliver AI power directly to devices, improving resilience and speed in constrained environments. By processing data step-by-step across a network of devices, they can create a safe and adaptable system that can withstand attacks and extreme conditions.
Researchers developed a hybrid approach combining molecular dynamics simulations and Helfrich theory to evaluate bending rigidities of graphene nanosheets with lattice defects. The study reveals insights for designing novel materials with tailored mechanical properties.
Zhuoran Qiao's AI-driven tool, NeuralPLexer, enables researchers to quickly model biomolecular interactions and predict drug binding pockets. This breakthrough accelerates the development of new drugs and healthcare.
Scientists use human-AI collaboration to tackle complex questions in condensed matter physics, leveraging machine learning algorithms to identify patterns in simulation data. This approach successfully models the behavior of frustrated magnets and sheds light on quantum computing and gravity.
The Hong Kong Polytechnic University has secured funding from the Research Grants Council to develop a cost-effective and sustainable collaborative Generative AI (Co-GenAI) model. The project aims to democratize AI development by reducing centralized computational demand and enabling broader participation in GenAI research and deployment.
Stanford researchers introduce a cost-effective way to evaluate AI language models, accounting for question difficulty to ensure fair comparisons. The new approach reduces costs by half and enables rapid progress in the field of artificial intelligence.
Researchers are creating generalized digital twins for machines, aiming to improve coordination up and down the production line. The center also plans to develop digital twins for human-robot collaboration and use software for simulation and analysis.
Researchers found that LLMs are better at representing non-sensorimotor concepts but struggle with sensory and motor concepts. Incorporating sensory input improves LLM performance and leads to more human-like representation, consistent with previous human studies on multimodal learning.
Hypergraph research aims to improve network resilience by analyzing multi-way relationships, enabling more accurate representations of complex systems. By modeling projects within Texas A&M University as hypergraphs, researchers can depict interconnectedness and how individual work affects the entire system.
A new design method, SPADE, evaluates UAV video surveillance system quality without real-world manipulations. It assesses the impact of image resolution on object detection performance., The SPADE method can be applied to various UAV systems and will aid in designing complex large-scale systems involving multiple UAVs.
Researchers have developed a world-first method to simulate specific types of error-corrected quantum computations, a significant leap forward in the quest for robust quantum technologies. The new algorithm tackles a long-standing challenge in quantum research and enables accurate simulation using conventional computers.
Researchers at the Wyss Institute have identified vorinostat as a promising treatment for Rett Syndrome using an AI-driven drug discovery process and innovative disease modeling. The findings demonstrate disease-modifying abilities across multiple tissues, offering hope for a potentially curative treatment.