Articles tagged with Mathematical Modeling
QUT researchers developed a new mathematical model to enable faster solutions for complicated problems in agriculture, ecology and medicine. The method improves how to determine the 'best' intervention strategies, balancing benefits and side effects, and can be applied to farming practices and chemotherapy.
Researchers analyzed 1.4 million field observations and 73,000 museum records to find a strong correlation between species abundance in nature and their presence in museum collections. This method enables scientists to study species decline and estimate past abundances, providing insights into conservation efforts.
A new study proposes that protocells, the putative ancestors of modern living cells, used temperature differences to divide. This simple mechanism could underlie the growth and self-replication of these early cells.
Scientists have discovered that geckos use their tails to recover from head-first crashes into rainforest trees, with implications for the design of agile robots. This versatile behavior allows geckos to stabilize themselves after impact and maintain control during gliding maneuvers.
Researchers develop novel models to investigate the impact of drug-eluting stents on arterial tissue permeability and blood flow. The study identifies key factors contributing to restenosis near stent edges, highlighting potential avenues for design improvements.
Researchers created a model to estimate viral population variability using epidemiological data, predicting the emergence of novel variants. The study suggests that lack of vaccination increases mutation frequency and spread.
A new study found that global marine ecosystem models differ widely in their representation of key processes, leading to underestimation of climate change impacts. Marine ecosystem models generally agree on biomass decline but disagree on magnitude and location across the world's oceans through the 21st century.
Researchers at Masonic Medical Research Institute are using human induced pluripotent stem cells (hiPSCs) to create scientific models for studying cardiac arrhythmias and testing therapeutics. The integration of mathematical modeling tools enables the prediction of drug efficacy with minimal adverse effects.
High-intensity focused ultrasound (HIFU) technology is being explored for its potential to destroy cancerous tumours while minimizing damage to healthy tissue. Researchers at the University of Waterloo have made significant progress in understanding how HIFU works on a cellular level, paving the way for future clinical trials.
Researchers explored optimizing disease control in prisons using rapid tests, identifying the optimal strategy to minimize costs and reduce infection. The study found that switching between full and no testing depends on various parameters, including contagion rates and test sensitivity.
A mathematical model developed by researchers at the University of Cambridge can predict the optimum exercise regime for building muscle. The model takes into account individual physiology and optimizes exercise regimes based on user input, promising to maximize athletes' potential.
A novel method for imaging vibrations and movements of atoms in catalysts has been developed by a collaboration of internationally leading researchers. The new analytical method reveals a dynamic behavior of the atoms, contrary to the long-held expectation that atoms in nanoparticles are static during observations.
A physics model simulates hand-washing to estimate the time scales on which particles like viruses and bacteria are removed from hands. The study finds that vigorous movement for about 20 seconds is necessary to dislodge these particles, consistent with traditional guidelines.
A KAUST-led research team has developed a prediction scheme that can more reliably forecast future environmental trajectories by integrating information from past complete and partial data. This approach, called partial functional prediction (PFP), captures both long-term trends and well-matched partial trajectories to achieve improved...
A new approach to modeling COVID-19 spread incorporates real-time data on people's movements, showing promise for optimal lockdown policies. The study suggests a balance between controlling the pandemic and minimizing economic costs can be achieved through mathematical models and Google mobility data.
A study at the University of São Paulo found that bird species interacting with more plant species have higher evolutionary stability. This is because they occupy central positions in seed dispersal networks, leading to longer lifespans and increased species accumulation.
University of Virginia researchers design a simple way to implement a tunable stiffness strategy in robots, enabling efficient swimming at varying speeds. The approach, inspired by the natural adaptability of fish, uses a programmable artificial tendon to adjust tail stiffness in real-time.
Researchers at Skoltech developed a mathematical model for thermoplastic composite materials, reducing conservatism in strength calculations. The model allows for virtual testing of structures, minimizing manufacturing costs while ensuring safety and quality requirements.
A new discovery explains what determines the number and position of genetic exchanges that occur in sex cells, such as pollen and eggs in plants or sperm and eggs in humans. This understanding is crucial for generating genetic diversity, evolution, fertility, and breeding technologies.
A new model incorporates human behavior and fears to predict multiple waves of COVID-19 infections. The 'Triple Contagion' model shows how fear can drive disease resurgence, as people may abandon protective measures prematurely or avoid vaccines due to misinformation.
Researchers have developed a new MRI methodology called Correlation Tensor Imaging (CTI) that can analyze stroke lesions with unprecedented accuracy. This technology may predict individual outcomes and guide treatment, improving patient care.
The University of Washington is leading a new NSF institute focused on using artificial intelligence to understand dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict. The institute aims to integrate fundamental AI theory with applications in critical technological areas.
SUTD researchers created a novel modeling technique to reduce risk of permanent device damage in resistive memory devices. The new toolkit can predict current accurately, improving prediction accuracy by around ten times.
A KAUST-led research team has developed an approach to mix high-precision calculations with lower precision for large geospatial datasets, significantly speeding up modeling without overall precision loss. The technique, implemented on high-performance computing systems, will enable larger datasets to be analyzed in shorter timeframes.
A new machine learning-based model predicts ICU patients' mortality risk based on characteristics such as demographics, comorbidities, and APACHE II score. The model overcomes traditional approaches' weak points, offering a better alternative for personalized medical predictions.
Scientists at Tohoku University have developed a new mathematical model to predict the properties of carbon-based materials. The Standard Realization with Repulsive Interaction (SRRI) model abstracts key effects and reveals relationships between changes and resulting properties.
Researchers developed a general theoretical approach to calculate the density of 5G base stations needed to achieve specific network parameters. The model shows that full isolation and mixed frequency regimes have different required densities, with the latter being more efficient but technically challenging.
A new modelling toolkit predicts local COVID-19 impact with unprecedented accuracy, using local hospital data to inform healthcare demand and capacity planning. The Halogen toolkit is available for other local authorities to use, enhancing their ability to predict infection levels and guide public health decisions.
Researchers discovered Romanescos are buds that never reach flowering state, resulting in stems producing new buds rapidly, creating pyramidal appearance and fractal structure.
A new study published in Emerging Infectious Diseases found that mathematical models informed early statewide policies in Colorado, avoiding 97% of potential hospitalizations. The modeling team developed a SEIR model calibrated to Colorado COVID-19 data and provided real-time estimates to policymakers.
Researchers from University of Bath's Institute for Mathematical Innovation offer free virtual workshop to solve challenges posed by holding large events safely. The three-day event will bring together mathematical scientists and other disciplines to model transmission rates, queuing risks, and physical interventions.
A new analysis identifies neighborhoods in England where dietary habits deviate significantly from recommendations, prioritizing urban areas in northern England and inner-city London. Targeted strategies such as vouchers for fruits and vegetables may ultimately reduce health inequalities in these areas.
A RUDN mathematician built a COVID-19 spread model to predict the disease's behavior and forecast subsequent waves. The model accurately predicted the epidemic course in countries without mass vaccination until the vaccination effect began.
The study found that cell-lineage maps are unlikely to be tree-like, with cross-links between different branches. The model also predicted the emergence of pluripotency, allowing for whole-body regeneration in many animals.
A RUDN mathematician has developed an algorithm that allows for the optimal distribution of computing tasks between IoT devices and the cloud, resulting in a reduction of power and time costs by about three times. The algorithm also protects devices from malware during data transmission.
Researchers have found that Rubisco proton production can enhance CO2 acquisition, allowing plants to fix more carbon dioxide and produce more sugar. This discovery could lead to improved crop yields and increased food security.
Researchers at Newcastle University have discovered new nutrient exchanges between algae and bacteria using advanced SIMS technique. The study shows that long-term carbon transfers occur between microalgae and bacteria, impacting microbial community functioning.
A new mathematical model provides unique insights into the multiscale biological alterations in the elderly and neurodegenerative brain, linking genetic changes to cognitive decline. The study identified genes contributing to healthy aging and Alzheimer's disease progression, with implications for future treatment targets.
A mathematical model predicts how bacterial mutations impact antibiotic success. The study suggests measuring mutation levels is crucial for determining treatment failure and informs novel strategies. Multiple mutations limit drug resistance evolution substantially, depending on drug type and administration route.
A mathematical model suggests repaying loans quickly can minimize cost, but delaying payments may be more beneficial for large balances. Borrowers should consider maximizing early payments and switching to income-based repayment when forgiveness is near.
Researchers developed a mathematical model to predict ice accretion on ships, helping improve safety in the Arctic climate. The model simulates the freezing of water droplets moving in cold air and their interaction with ship surfaces.
A new mathematical model developed by Chalmers University of Technology can predict how different bacteria interact and affect the intestinal microbiome. The model was tested on two clinical studies, one involving Swedish infants and another adults in Finland, showing high accuracy in predicting multiple variables.
A new framework connects metabolic principles to forest patterns, revealing regularities beneath apparent randomness. The model allows for generalization to diverse species and situations, providing insights into forest structure and biological complexity.
Researchers found that competition for resources led to the development of vastly different sizes between egg cells and sperm cells. The study used mathematical modeling to demonstrate how natural selection favored larger gametes due to increased nutritional value, while smaller gametes required fewer resources.
Researchers at Tomsk Polytechnic University developed a mathematical model and software to predict ClO2 molecule properties with high accuracy, surpassing existing results by 10 folds. The model was applied to analyze rotational-vibrational spectra in a degenerate electronic state, showing promising results.
The Canadian Network for Modelling Infectious Disease (CANMOD) will inform public health decisions and prepare Canada for future pandemics. CANMOD will build and coordinate national capacity by sharing research problems, models and estimates across a broader community of researchers.
A retrospective analysis of 198,846 hospitalizations in France from March to November 2020 reveals major changes in the age and sex of hospitalized patients during the pandemic. The probability of ICU admission and death varied substantially over time, with a decrease followed by an increase in both cases.
A mathematical model reveals that people tend to categorize themselves and others along a spectrum, excluding those in the middle. The study predicts that self-identified Democrats and Republicans view political independents unfavorably, lumping them in with the other side or perceiving them as truly in-between.
A new mathematical model suggests that easing lockdown must be accompanied by wider and more effective use of control measures like facemasks to suppress COVID-19. The model shows that non-spatial control measures can limit virus particle spread between people.
A new study found that face mask distribution and mandatory use corresponded with a significant reduction in viral transmission in Italy. The use of face coverings may have reduced official COVID-19 cases by up to 30,000 during the first half of 2020.
Scientists at the University of Exeter made a breakthrough in understanding brain activity during seizures by studying synchronization patterns. The study found that excessive synchronization can lead to epileptic seizures and Parkinson's disease, impairing brain function.
A new model developed by a Washington University mathematician helps estimate the relative risk of different in-person activities, such as traveling or attending events. By breaking down individual factors and adding their contributions to relative risk, the model allows for informed decision-making about participation.
Researchers developed a mathematical model to simulate SARS-CoV-2 infection, finding innate immunity controls viral load more effectively than adaptive immunity. The study suggests starting antiviral or interferon therapy as soon as symptoms appear.
African termites' underground nests are built using a self-organized architecture that reflects individual behavior and environmental cues. The study's mathematical model simulates nest construction, revealing consistent vertical and horizontal spacing patterns.
A new study published in Nature Communications found that deep learning models surpass standard machine learning approaches in analyzing brain imaging data, generating more accurate representations of the human brain. This is particularly beneficial for complex problems requiring large datasets and advanced analysis.
A traditional disease spread model may not accurately predict COVID-19's continuing spread, especially during lockdowns. Researchers suggest a dynamic approach using the geometric mean to better forecast short-term numbers.
Researchers from Universidad Carlos III de Madrid and UNED used big data and physics research to analyze Beethoven's metronomic marks. The study found that conductors tend to play slower than indicated, with a systematic deviation, which could be explained by the composer reading the metronome incorrectly.
Researchers at St Petersburg University have developed a new Case-Based Rate Reasoning (CBRR) model to predict epidemic dynamics. The model uses data from countries where the disease was recorded earlier to forecast its spread in Russia, and is being updated weekly for cities like St Petersburg and Moscow.
New theories have enabled researchers to model unusual dynamics where particle motions are no longer influenced by previous events. The 'memory term' principle allows studying this effect in a broader range of situations, particularly for advanced materials that respond to their environment.
A RUDN University physicist created a software solution to identify the instability regions of black holes, ensuring their mathematical models are physically viable. The approach uses Einstein's equation with added corrections and identifies critical coupling constants that affect model stability.