Articles tagged with Computer Modeling
A new study published in Nature Communications reveals chemical heterogeneities in Apollo 17 sample troctolite 76535, indicating an early rapid cooling of the Moon. This finding challenges previous estimates of a 100-million-year cooling duration and supports initial rapid cooling of magmas within the lunar crust.
Researchers developed an algorithm to differentiate life-threatening gunshot events from non-life-threatening plastic bag explosion events. The study found that 75% of plastic bag pop sounds were misclassified as gunshot sounds, highlighting the need for a diverse dataset of similar sounds.
A new AI algorithm, APOLLO, accurately predicts microprocessor power consumption by analyzing just 100 signals out of millions, offering potential to improve efficiency and develop new processors. The technique has been validated on high-performance microprocessors and could help designers inform future chip design.
A new study suggests that strictly limiting exposure to dissimilar views could be an effective mechanism for avoiding rapid polarization. Another interesting finding is that a small amount of self-interest can dramatically reduce polarization.
Researchers developed an AI-powered system to model disease progression in patients as they age. The model assesses metabolic and cardiovascular biomarkers to calculate health status and disease risks across a patient's lifespan.
A new study published in the journal Anatomical Record reveals that gharial species, native to India, suffer from nasal septal deviation, a condition common among humans. The researchers found that this deviated septum affects the breathing rate and may lead to high shearing stresses along the nasal walls.
A team of astrophysicists led by Boston University's Merav Opher has made a groundbreaking discovery about the heliosphere, the bubble surrounding our solar system. Their research suggests that neutral hydrogen particles play a crucial role in shaping the heliosphere's structure and stability.
A deep-learning model called D-SCRIPT predicts protein-protein interactions from primary amino acid sequences with high accuracy. The model enables the detection of functional subnetworks and can be applied to many species, including those with rare or lacking PPI data.
The University of Texas at Arlington has received a $1.5 million grant from the US Department of Education to support domestic doctoral students specializing in Internet-of-Things study and research. The project aims to increase diversity among computer science researchers, with a focus on women and minorities.
A $4.5 million civil engineering study simulates the impact of explosives on structures using a virtual reality platform to develop strategies for combat operations and protect people. The research, led by Hussam Mahmoud, aims to create more resilient structures that can withstand blasts and other forms of damage.
Detailed supercomputer simulations show that the North American monsoon occurs when Mexico's Sierra Madre Occidental mountains divert the eastward-trending jet stream toward the equator, cooling moist tropical air until it condenses and falls as rain. This new understanding will impact forecasts in the region.
Research reveals Arctic rivers are discharging much more water than previously thought, with acceleration up to three times higher than estimated. The RADR project assimilates satellite information into hydrologic models for the entire pan-Arctic region.
A team of researchers has developed a polymer that can form folded (ordered) and unfolded (disordered) domains using UV irradiation. The polymer's structure is controlled by non-bonding interactions between monomers, allowing it to be manipulated after formation.
Researchers at Aarhus University have developed a groundbreaking technology to accurately map and characterize groundwater systems. The new method uses surface-based NMR measurements, enabling detailed hydrogeological and geological analysis in previously inaccessible areas.
A new study suggests that social distancing alone is ineffective in mitigating COVID-19 spread and recommends vaccination, ventilation, and masks as vital measures. The researchers found that individual coughs vary widely, leading to a 'safe' distance of anywhere between one to three or more meters.
UC San Diego researchers developed a computer model to understand how viruses travel through the air, shedding new light on airborne COVID-19 transmission. The model simulates aerosol behavior and helps explain why SARS-CoV-2 is highly transmissible through the air.
Fred Martin, a UMass Lowell computer scientist, is being recognized for developing an educational framework that introduces robotics and AI to K-12 students. His work has guided the development of primary and secondary AI education initiatives worldwide.
A new data-model study suggests that the Antarctic Ice Sheet's tipping point was reached within a decade, leading to centuries of ice mass loss and potential irreversible ice retreat. The research team analyzed sediment cores and computer models to identify evidence of post-glacial tipping points in the past.
Researchers found that re-identifying individuals from genomic data using public face images is harder than previously thought, with success rates well below idealized settings. They developed a method to alter social media photos and reduce the risk of privacy breaches.
A research team analyzed seismic noise recorded over the last decade to better understand volcanic processes at Campi Flegrei. They found that directionality loss in noise data indicates the migration of deep fluids towards the eastern caldera, which triggers earthquakes.
A multidisciplinary team from UCI has published a new guide for statistical analysis in neuroscience research, providing an overview of mixed-effects models and clear instructions on their application. The guide aims to improve the validity and reproducibility of experimental findings in the field.
Researchers from Okayama University developed an AI-powered image classifier to simplify and speed up the task of image analysis in cell biology. The system achieved high detection accuracy for mitotic cells in plant species, demonstrating its potential for non-experts to use.
International researchers used machine learning to forecast marsh establishment under various environmental conditions, revealing that controllable local factors are more important than global climate change. The study suggests smart management of tidal flats can counteract threats and strengthen wetlands.
A new Illinois study compares performance of static and dynamic nitrogen recommendation tools, finding that complex dynamic tools do not consistently increase profits but offer a 15% reduction in nitrogen leaching. The researchers highlight the need to refine these tools and prioritize environmental outcomes over economic gains.
Researchers have developed AI-powered computer models to predict heart attacks, strokes, and stress-induced blood pressure changes. The models use machine learning to detect early signs of aortic valve anomalies, enabling at-home monitoring and improved diagnostic accuracy.
Researchers developed AfriBERTa, a neural network model that achieves state-of-the-art results for low-resource African languages. The model works with 11 languages spoken by over 400 million people and requires only one gigabyte of data, compared to thousands for existing models.
A statistical modeling approach highlights predictive importance of local climate and human activity in determining wildfire probabilities. The analysis uncovered key roles for climate, human activity, fire history, and short-term climate variations in fire-prone regions.
Researchers used computer simulations to predict the presence of hydroxyl radicals, which clean pollutants from the atmosphere. The study showed that traditional models had widely varying forecasts due to uncertainties in gas emissions, and that better models can aid in combating climate change.
Researchers create a multiscale model to track water quality indicators like nitrogen and mercury levels, incorporating biogeochemical reactions in microbially-active zones. They also develop 'stretchier' alloys by adding nano structures, which enhance strength and ductility, making them suitable for various applications. Additionally,...
Research at Kyoto University using drones and a multi-agent system found that feral horses synchronize their behavior not only within unit groups but also across different parts of the herd. The study's results suggest that individual horses can recognize the behavior of distant individuals, averaging a nearest distance of 39.3m.
A new study by Virginia Institute of Marine Science researchers maps the conditions most suitable for key species of forage fishes in the Chesapeake Bay, offering guidance for protecting or restoring their habitats. The analysis reveals that factors such as water depth and temperature affect the abundance of forage fish, with some spec...
Researchers from Oregon State University have found that citizen scientist contributions significantly improve snowpack modeling, particularly in high mountain environments. The study leverages data collected by thousands of citizen scientists through the NASA-funded Community Snow Observations project.
A new study reveals that high-performing AI next-word prediction models resemble the function of language-processing centers in the human brain. The models' activity patterns closely match those seen in the brain during language tasks, suggesting a potential connection between AI and human language processing.
A computer model developed by the University of Surrey researchers supports Bernard Williams' 40-year-old hypothesis that pressure changes cause fluid in the cavity to 'slosh', generating stress in the spinal cord tissue. The study shows that this process can lead to small cavities gradually expanding down the spinal cord.
A new study suggests that patient-specific plates can provide superior performance in terms of anatomical fit and fracture stability. Custom-made plates designed through computer modeling have shown potential in improving healing outcomes for clavicle fractures.
Researchers created EpiPolicy software system to model epidemics, maximizing public health protections while minimizing economic and social costs. The model uses Monte Carlo and reinforcement learning techniques to design intervention strategies automatically, taking into account risk and uncertainty.
A new study from Cornell University uses digital twins to identify optimal times and locations for detecting Listeria bacteria in produce facilities, reducing the risk of contamination. The researchers' model provides a novel way for food safety managers to visualize microbial contamination risks and experiment with different sampling ...
Researchers at H. Lee Moffitt Cancer Center & Research Institute use computer modeling to study the relationship between tumor-immune environment and radiation response. They discover that tumor cells will either escape immune predation or be eradicated based on the numbers of immune effector and suppressor cells present. The model hel...
Researchers have demonstrated ultrafast optical circuit switching for datacenters using integrated soliton microcombs, which can handle increasing bursty datacenter applications while reducing overheads. The proposed architecture employs a central comb system to improve power efficiency and reduce complexity.
Researchers developed a new risk measure, Universal Influenza-like Transmission (UnIT) score, that outperformed existing models in predicting weekly case count forecasts. The model used 10 years of influenza hospitalization data to identify patterns and improve COVID-19 spread predictions.
A recent study suggests that primates and marsupials were among the few tree-dwelling mammals that survived an asteroid impact 66 million years ago. The researchers used computer models and fossil records to find that most surviving mammals did not rely on trees, but some arboreal species may have been versatile enough to adapt.
Researchers study entangled blackworm blobs that show 'intelligent' collective movement when conditions are just right. The model suggests a fine balance between individual movement and clinginess is needed for sustained collective movement.
A team of researchers from the University of Illinois Urbana-Champaign used advanced machine learning to model the physico-chemical properties of a molten salt compound called FLiNaK, enabling accurate atomic-scale reproduction and prediction of behavior under specific reactor conditions. This computational framework can help character...
Researchers developed a novel machine learning algorithm to identify previously unknown air pollutant mixtures linked to poor asthma outcomes in children. The study found that early exposure to individual and mixed pollutants can lead to longer-term problems with asthma, affecting about seven percent of US children.
Researchers from McGill University found that unmasked individuals can spread COVID-19 airborne particles up to 70% within a 2-meter radius, highlighting the need for mask-wearing and ventilation. The study suggests that current guidelines relying solely on physical distancing may not be sufficient to prevent transmission.
A $1 million NSF Award supports a project to create an open underground infrastructure map to improve disaster resilience in New York City. The Unification for Underground Resilience Measures (UNUM) project will standardize data sharing and visualization, enabling faster response times and increased safety.
Researchers have developed patient-specific whole-heart models to predict risk of sudden cardiac death and outcomes of cardiac procedures. These models incorporate individual geometry, structure, and other patient-specific information, enabling personalized diagnosis and treatment.
Researchers developed a feature selection algorithm that uses boosting to select relevant features from high-dimensional data sets. The algorithm outperforms other methods in terms of accuracy and number of features used, making it more scalable and explainable.
A team of researchers, led by University of Houston associate professor Ryan Kennedy, has received a $750,000 NSF grant to create an algorithm-accountability benchmark. The project aims to establish general ways of analyzing algorithms and studying their impact on public policy decisions.
Researchers developed a computational model to optimize the use of additive manufacturing in spare parts supply chains, finding that centralized systems are more efficient for less frequent demand with high variability. The study's findings provide a step forward in understanding how to incorporate AM technologies into supply chains.
A Rice University professor has received a $2.4 million grant from the NIH to develop open-source software for designing personalized treatments for movement impairments using computational modeling and simulation. The software will create customized computer models of individual patients, optimizing treatment solutions.
A team of NTU Singapore scientists developed a predictive computer model called NSGA-II, which proposed strategies to reduce COVID-19 infections and deaths by an average of 72%. The model recommended timely and country-specific advice on interventions such as home quarantines and social distancing measures.
Researchers at MIT and Institut Pasteur have created an efficient method for assembling entire genomes, including the human genome, in minutes using personal computers. This approach uses minimizer-space de Bruijn graphs to store only a small fraction of nucleotides while preserving overall genome structure, enabling faster processing ...
A mathematical model developed at Linköping University predicts that many species in polar regions will become extinct due to global warming, with ecosystems already showing signs of strain. The model simulates the impact of climate change on species interactions and diversity, highlighting the importance of considering ecological proc...
Five innovative research projects tackle fundamental questions of environmental and earth science, including the origins of Earth and life on Mars. The studies aim to advance our understanding and lead to important scientific breakthroughs.
Researchers developed WebFRIS, a web-based platform to provide socio-economic vulnerability analysis and simplify flood risk information. The tool can help formulate targeted solutions and enhance social capacity and resilience in flood-prone regions.
Researchers developed a new computational procedure to discover functional protein shapes, revealing molecular details of cellular processes. The approach focused on allosteric regulation and applied to adenylate cyclase, a key enzyme in energy metabolism and hormone signaling.
Researchers found that the stability of an amorphous metal alloy's structure is disrupted by mechanical influences, leading to crystalline inclusions. The alloy retains useful properties at pressures below 400 gigapascals before experiencing rapid crystallization and loss of structural integrity.
Researchers successfully simulated the capsid disassembly step of hepatitis B viral infection at an unprecedented atomic level, identifying specific regions of the capsid protein that contribute to breakage. This high-accuracy simulation can help design drugs to interrupt these processes and prevent chronic infection.
A new platform called LEARNER allows researchers to share private patient data from multiple institutions while protecting privacy. This enables single institutions to access advanced predictive tools and improve patient outcomes.