Articles tagged with Mathematical Modeling
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.
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.
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.
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.
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 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 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.
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.
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.
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 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.
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.
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 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.
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.
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.
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.
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.
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.
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 ...
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 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.
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.
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 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.
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.
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 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 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...
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.
The study reveals the sing saw uses a surprising effect to create its distinct tone: when curved into an S-shape, energy vibrates in a confined area producing a clear, long-lasting sound. This principle can be applied to design high-quality resonators for various applications.
Researchers are studying moths to understand agile decision-making in complex environments, aiming to develop autonomous bio-inspired systems. Meanwhile, a team is exploring origami- and kirigami-inspired flexible structures for multifunctional applications.
The new computational tool, AF2Complex, predicts the structure of protein complexes and their interactions, offering insights into biomolecular mechanisms. The model is based on AlphaFold 2 and performs well in predicting protein structures and complex formations.
A study by Tokyo University of Science researchers has demonstrated that a computationally-light model can simulate complex brain cell responses, including periodic and quasi-periodic responses. The Izhikevich neuron model was found to be capable of reproducing both types of responses at lower computational cost.
A new KAUST study uses machine learning to predict disease spread with high accuracy, dynamically incorporating latest data without human bias. This approach offers a promising alternative to conventional models, providing a more accurate story of the underlying epidemic data.
A team of researchers, led by L. Mahadevan, developed a mathematical model to understand the mechanics of combing and detangling. They found that using short strokes starting from the free end can effectively remove tangles, with an optimal minimum length for each stroke identified.
A new mathematical model predicts a significant shift in US public opinion on moral issues, with abortion rights expected to dominate. Within 15 years, a majority of Americans will support equal parental leave.
Researchers used mathematical modeling to characterize the interplay between immune suppression, immune blockade and antigenicity in pre-cancerous cells. They found that an immunosuppressive environment is crucial for colorectal cancer progression.
Researchers have discovered an elegant equation to approximate the coherence time of materials hosting spin qubits. The team can now estimate coherence times in seconds using just five material properties, facilitating a rapid exploration of new candidate materials.
A novel 'rational' neural network reveals underlying mathematical equations through Green's functions, enabling humans to understand machine-generated findings. This breakthrough in partial differential equation learning holds promise for advancing scientific exploration of weather systems, climate change, and more.
New research suggests the brain uses multiple strategies to process smells, employing both snapshot-like and evolving ensemble approaches. The study provides new tools for scientists to quantify and interpret brain activity patterns.
A new study used a virtual population to simulate a clinical trial examining kidney damage in Black Americans. The model predicted kidney damage and showed that adding drug therapy and reducing salt intake could stop the damage, improving heart sizes as well.
The project will develop a better understanding of nuclear radiation by integrating recent mathematical developments into radiation transport modelling. This will lead to improved nuclear safety, cancer radiotherapy, and shielding for astronauts from cosmic rays.
Researchers have developed a new forecasting approach using machine learning and anonymized datasets from Facebook that significantly outperforms conventional models for projecting COVID trends at the county level. The model captures shifting trends in numbers reflecting lockdowns, waning immunity, or masking policies.
Researchers at Ural Federal University have developed a method to significantly accelerate the synthesis of aluminum-based alloys using computer modeling. The new approach allows for control over the internal structure and physical properties of the alloy, enabling the creation of materials with desired characteristics.