Articles tagged with Mathematical Modeling
Researchers have developed a new model that combines nuclear physics and string theory to describe the transition to dense and hot quark matter in neutron star collisions. The model allows for the calculation of gravitational-wave signals, showing that both hot and cold quark matter can be produced.
Physicists at Ural Federal University have developed a theory regulating the solidification of iron-nickel alloys to control characteristics and improve uniformity. This technology will affect high-precision instruments like clocks, seismic sensors, and engines.
A new study in PNAS suggests that the spread of COVID-19 in US counties followed a hybrid lognormal-Pareto distribution, with 99% of counts conforming to Taylor's Law and the top 1% matching the Pareto distribution with infinite variance.
Researchers from University of Colorado Boulder develop equation learning technique, WSINDy, to grasp how body rebuilds skin and accelerate wound healing. The method can apply to various phenomena in the natural world, including flocking behavior in birds.
A new study found that increasingly complex mathematical models can produce more uncertain estimates, limiting their usefulness for informing real-world policy decisions. Researchers recommend reassessing the drive to create detailed models and suggest calculating the model's effective dimensions before adding complexity.
A mathematical model suggests that social interactions between sedentary and moderately active people can stimulate higher levels of overall physical activity in a population. The model found that interacting with moderately active individuals helped sedentary populations become more physically active over time.
Researchers at the University of Technology Sydney have extended the theory of acoustic levitation to account for asymmetrical particles, which is more applicable to real-world experience. This new understanding enables precise control and sorting of tiny objects using ultrasonic waves.
A recent review article summarizes the latest developments in finite-control-set model predictive control (FCS-MPC) strategies for PMSMs. FCS-MPC is a promising approach to optimize drive systems, but challenges remain, including computational complexity and parameter uncertainty.
A breakthrough computer model from Chalmers University of Technology reveals the properties of an atomic nucleus, providing insights into the strong force that governs neutron star behavior. The model predicts a surprisingly thin neutron skin, which could lead to increased understanding of heavy element creation in neutron stars.
Researchers developed computational models to identify massage businesses at risk of violating laws related to sex and labor trafficking. The models provide probability scores on the likelihood that a business is engaged in illegal activity, allowing law enforcement and organizations to prioritize investigations.
Mathematicians at Aston University are developing computational modelling techniques to examine biological drag reduction methods, such as fish slime secreted by predator-avoiding fish. This project aims to reduce skin-friction drag and increase the performance range of electric vehicles, contributing to a reduction in CO2 emissions.
Aneurysms occur when blood vessel thins and expands, leading to increased stress on walls and rising rupture probability. The new model uses computed tomography scans to reconstruct geometry and blood flow patterns.
A team of scientists from the University of Exeter has made a key breakthrough in predicting fluctuations in the rotation of the Earth and the length of the day. They used mathematical modeling to show that changes in the atmosphere can be predicted more than a year in advance, linking geodesy with climate prediction.
Physicists used machine learning to compress a complex quantum problem into four equations, capturing the physics of electrons on a lattice with high accuracy. The approach could revolutionize how scientists investigate systems containing many interacting electrons and potentially aid in designing materials with sought-after properties.
A team of scientists from Ural Federal University has developed a complex mathematical model to understand the dynamics of nanoparticles and viruses in cells. The model reveals how viruses cluster inside endosomes and interact with cellular proteins, shedding light on their behavior and replication mechanisms. This breakthrough can hel...
The new equation developed by University of Bristol scientists can be used to model accurately particle motion through porous materials like biological tissues, polymers, rocks, and sponges. This could lead to exciting advances in medical procedures, natural gas extraction, and plastic packaging production.
Xiu Yang, a 2022 NSF CAREER award recipient, is working on an algorithmic approach to model and overcome hardware errors in quantum computing. He aims to enable the technology to achieve its promise of unparalleled speed in solving complex problems.
Researchers developed a mathematical model of cilia beating due to mechanical instability caused by the cilium motor protein dynein. This knowledge will aid in understanding and treating cilia-related diseases.
Physicists at Ural Federal University developed a mathematical model to predict El Niño's behavior, accounting for wind, humidity, temperature, and ocean currents. They found that stronger winds can cause unpredictable weather phenomena.
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.
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.
A new mathematical framework predicts the most effective combinations of drugs to treat postmenopausal bone loss in patients. The model identifies optimal orderings that boost bone density and reduce long-term bone loss, improving treatment outcomes.
Researchers at Princeton University used artificial intelligence to simulate ice formation by individual atoms and molecules with quantum accuracy. This breakthrough enables tracking of hundreds of thousands of atoms over longer timespans than previous simulations.
The researchers developed a versatile model that takes into account factors such as infection rates, mortality, and recovery. They found that limiting contacts through quarantine is effective in reducing disease incidence and suppressing the virus.
Research suggests that persistent viral infections within hosts can lead to long COVID or post-acute sequelae of COVID-19. The study found that systemic infection and compromised immunity are key factors enabling persistent infection.
Researchers at University of Bristol developed mathematical models to assess biomarkers for detecting glioblastomas, a type of brain cancer. The study found that lowering the current biomarker threshold could lead to earlier detection using blood tests.
The team is creating a virtual gut system to test chemical reactions and networks, which will help identify the best treatments for obesity. The MATOMIC project aims to develop mathematical models that can simulate the intestinal microbiome at an atomic level.
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.
Researchers at Ural Federal University developed a mathematical model explaining anomalous behavior in melts, which can lead to creating materials with specific properties. The model accounts for nucleation and crystal growth, reducing supercooling and narrowing the two-phase layer.
Researchers at Moffitt Cancer Center used mathematical modeling to study the impact of tumor size, immune cell populations, and interactions on cancer treatment outcomes. The models predicted optimal therapeutic approaches for different types of therapies, including cytotoxic chemotherapy and immunotherapies.
Researchers at UNSW Medicine & Health unveil a simple mathematical model predicting DNA hybridization rates based on nucleating interactions. The discovery has the potential to improve understanding of biological systems and refine nanotechnologies.
Research suggests that climate change is causing dryland mechanisms to affect temperate regions, leading to changes in vegetation distribution and ecosystem processes. The study predicts that by the end of the century, an estimated 17 million km2 of non-dryland areas will experience average topsoil temperatures above 40°C.
Researchers developed a mathematical model to predict the efficiency of nanoparticle delivery into cells, particularly in stem cells. They found that nanoparticles become trapped in bubble-like vesicles, preventing them from reaching their targets.
A new general theory describes how temperature affects living things at all scales, predicting patterns in metabolism, growth rate, and mortality rate. The model combines elements lacking in earlier attempts, providing a universal framework for understanding temperature's impact on biology.
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.
A team of scientists discovered a mathematical principle explaining how cells connect to form tissues and organs, shedding light on embryonic development and organ formation. The study found that epithelial cells can adopt complex three-dimensional shapes like scutoids, which determine cellular connectivity and tissue properties.
Researchers at Texas A&M University are developing mathematical models to predict and control cellular differentiation. They created a technique using mix-and-read assays, which allow for the detection of key signaling proteins in live tissues. This method enables researchers to gain a deeper understanding of how cells make decisions.
Researchers developed a nomogram to predict anal cancer risk in persons with HIV infection, highlighting increased risk among those living with the virus. The model estimates quantitative risk based on patient characteristics, informing screening decisions.
New research suggests that stalling improvements in cardiovascular diseases since 2010 could lead to significant economic costs. The study finds that reduced cardiovascular disease rates would result in £13 billion NHS healthcare costs, £1.5 billion social care costs, and £32 billion lost QALYs between 2020-2029.
An international research team applied a new dynamical model and exceptional historical data to examine major explanations for cultural complexity evolution. The study found little support for many influential theories, including the transition to agriculture and conflict theories.
A mathematical modelling study published in The Lancet Infectious Diseases estimates that COVID-19 vaccines have prevented approximately 19.8 million deaths worldwide in the first year of the vaccination program, a reduction of 63%. This represents nearly 80% of potential deaths that would have occurred without vaccination.
Researchers developed a mathematical model to predict COVID-19 transmission in train carriages, finding that risk is constant regardless of location and that masks are more effective than social distancing in reducing transmission. The study highlights the importance of improving ventilation systems on trains to keep passengers safe.
Researchers at Ohio State University created a mathematical model that assesses the efficacy of immunomodulatory drugs in treating myocardial infarctions. The study found that certain combinations of these drug inhibitors were more efficient at reducing inflammation, offering new possibilities for improving patient outcomes.
EpiMoRPH aims to develop locally customized models for decision-making, addressing the limitations of previous disease modeling systems. The project will use two virus-based diseases as case studies and provide a framework for characterizing meta-population disease models.
The five-year grant will support research on foundational theory and real-world applications, including simulation of optical fibers, weather forecasting, and disease understanding. Postdoctoral researchers and graduate students will work with faculty mentors to develop data acumen and prepare for Ph.D. research.
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 new study from the University of Kansas found that a professional development intervention improved an educator's teaching skills and boosted students' abilities to solve word problems, especially for Hispanic English-learning students with math learning disabilities. The program helped develop a 'feedback loop' between educator and ...
A new study reveals that large-scale hydrological models producing global irrigation water withdrawal estimates are unreliable due to overlooking uncertainties and traditional irrigator practices. The researchers argue that these biases can lead to grave policy misjudgments, devastating consequences and inaccurate water management.
Researchers have developed a new formula for swimming based on their study of odd elasticity, allowing microswimmers to exhibit autonomously directional and deterministic motion. The team used Purcell's swimmer model to demonstrate that any odd elastic micromaterial can spontaneously generate locomotion in a fluid.
A new study examines mathematical models designed to draw inferences about how evolution operates at the level of populations of organisms. The researchers conclude that such models must be constructed with care, avoiding unwarranted initial assumptions and weighing existing knowledge.
A new study from Aarhus University challenges the traditional 'cry curve' pattern of infant crying, which suggests a sharp decline in crying after six weeks. The research, pooling data from 57 studies across 17 countries, reveals that infants continue to cry significantly after six months.
A new distributed learning technique, GD-SEC, reduces communication requirements in wireless architecture, improving efficiency and reducing computational cost. The method employs data compression to transmit only meaningful, usable data, enhancing the impact of machine learning while minimizing its limitations.
Researchers found tree growth not source-limited but rather by cell growth, suggesting forests may not absorb as much carbon as thought. The study's findings challenge current forest growth models and highlight the need for climate change mitigation strategies.
A study led by Washington University in St. Louis found that nonlethal parasitic infections reduce herbivory rates and trigger trophic cascades impacting plant communities. The research used a mathematical model and global meta-analysis to highlight the ecological consequences of common parasitic infections in wild animals.
Researchers developed an ensemble model combining multiple individual predictive models to anticipate COVID-19 hospital demand, achieving a significant reduction in prediction error. The study's findings show that incorporating early predictors such as epidemiological data and mobility data leads to better forecasts.
Researchers at H. Lee Moffitt Cancer Center & Research Institute have identified key factors impacting the effectiveness of adaptive therapy in cancer treatment. The study found that spatial organization and competition between drug-resistant cells play a crucial role in determining the success of adaptive therapy.
Researchers at DTU Compute and DIKU have developed a machine learning model that can map the potential of proteins, enabling the biotech industry to accelerate the development of new proteins. The model generates a picture of how proteins are linked, allowing for the identification of closely related proteins with desirable properties.
A new mathematical model suggests that wearing masks could drastically slow the spread of COVID-19, reducing severe cases and pressure on healthcare systems. The study's findings are potentially applicable to any respiratory infection transmitted by inhaling infectious particles.
A mathematical model reveals that spontaneous symmetry breaking in chemical reactions leads to homochirality, optimizing energy harvesting from the environment. This phenomenon could explain how life developed on primordial Earth and has implications for the synthesis of chiral drug molecules.
A new study published in PLOS Global Public Health found that shielding vulnerable populations while allowing the virus to spread through the rest of the population would have failed. The research reveals that even with perfect shielding, healthcare capacity would have been overwhelmed, and tens of thousands more deaths would have occu...