Articles tagged with Mathematical Modeling
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 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 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 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.
A new election forecasting approach uses a mathematical modeling technique to describe how voters in different states may influence each other during an election year. The model forecasts a victory for Biden 89.03% of the time, with a margin of victory lower than the percentage of undecided voters.
A new study by Ashish Goyal and colleagues proposes that treating with potent antiviral therapy before peak viral load can effectively suppress viral AUC, reducing the duration of viral shedding. The model also suggests that timing is crucial in determining the effectiveness of different therapies against COVID-19.
Researchers developed a new mathematical framework that leverages uncertainty and expert knowledge to create more accurate and efficient computer models. This method provides guarantees on model performance and can lead to breakthroughs in renewable energy, battery technology, and other fields.
A RUDN University mathematician has enhanced the standard predator-prey model to include nonlocal interactions, which affect system dynamics and lead to different patterns of predation and prey growth. This improved model will help ecologists better understand natural systems and predict their development.
A mathematical model reveals human spaceflight reduces exercise tolerance and ages astronauts' hearts, with features similar to accelerated aging. Understanding 0G configuration is crucial for ensuring astronaut health in future missions to the Moon and Mars.
Researchers developed a mathematical model to estimate key physiological parameters like maximal aerobic speed and endurance in athletes. The non-invasive data gathering method offers new possibilities for treatments and monitoring.
Researchers developed a comprehensive mathematical framework to optimize sensor placement and selection. The model revealed that not all sensors were needed to accurately estimate key physical states, such as velocity and angle of attack. This approach balances cost and precision, making it a critical solution for complex systems.
A new dynamic mathematical model developed by researchers from the Universitat Autonoma de Barcelona has shown that it can accurately predict the probability of mortality and survival of small species under heat stress. The study's findings suggest that the current standard model subestimates species' vulnerability to climate warming a...
New research shows that US political parties have become increasingly polarized due to their quest for voters. The study challenges a 1950s model by economist Anthony Downs, finding that voters remain moderate but parties move towards extremes to minimize constituency overlap.
Texas A&M researchers have created a contagious model to accurately forecast flood water spread and recession process on urban road networks. The findings provide valuable insights into the universality of network spread processes across various systems, which can help better manage cities.
A mathematical modeling study examines the potential of contract tracing to combat SARS-CoV-2 transmission under varying assumptions. The researchers found that effective contact tracing can significantly reduce the spread of COVID-19 in scenarios with reduced physical distancing.
The study proposes a new perspective on urban development, revealing the complex interplay of individual and collective actions that drive meaningful change. It highlights the slow pace of change in cities and the negative impact of uncertainty and policies promoting aggregate growth on innovation and inequality.
Academics from City University London, led by Professors Bev Littlewood and Martin Newby, are improving the quality of epidemic models using Software Engineering Boards (SEBs). The project aims to increase confidence in decision-making based on mathematical models, ensuring policy-making security and improved access to models.
Tyrus Berry is developing a semiparametric modeling framework to leverage strengths of parametric and nonparametric methods. The project aims to provide flexible models with quantifiable uncertainty for optimal predictions in limited noisy observations.
Researchers at Kyushu University developed a mathematical model to evaluate two hepatitis C virus strategies, finding that one strain prefers to multiply while another prefers to spread. The study provides new insights into viral behavior and could help develop effective therapeutic methods.
Historical modelling analysis suggests that community-led interventions, including public hygiene initiatives and social distancing measures, played a crucial role in controlling the Typhus epidemic in the Warsaw Ghetto. The study highlights the importance of grassroots efforts in combating infectious diseases.
Scientists from CNRS and University of Nantes have developed a mathematical model to predict the risk of epidemic emergence based on seasonal factors. The 'winter is coming' effect suggests that pathogens introduced before unfavorable winter periods are less likely to cause large epidemics.
A Columbia data scientist aims to develop tools to manage demand with flexible options for consumers, converting their flexibility into operational efficiency for companies. He plans to create mathematical frameworks to optimize pricing and scheduling, benefiting both consumers and firms.
The study found that applicant practices were diverse but lacked representation from small, independent, rural, or inexperienced practices. Participating practices served healthier and more advantaged beneficiaries, highlighting the need for further engagement.
A new model of opinion formation shows how people's emotional connections with others influence their political views, leading to increased polarization. The Weighted Balance Theory identifies hyperpolarization as a vicious circle where intense emotions and opinions replace moderate positions.
Researchers from Russia and the Czech Republic modelled natural rock arcades, finding that they appear in areas with discontinuities due to erosion. The study used a mathematical model that describes the formation of arches and pillars through weathering and erosion processes.
Researchers developed a mathematical model to describe human behavior in various scenarios, helping to understand decision-making. The study found that people tend to use a linear threshold model, increasing the price they're willing to pay over time.
Researchers applied a new Gaussian mathematical model to high-precision laser 40Ar/39Ar dating data from the Tengchong area, resolving 3 volcanic eruptive stages: Pliocene (3.78 Ma), early Middle Pleistocene (0.63 Ma) and late Middle Pleistocene (0.139 Ma). The method reduces uncertainty and defines stages with high precision.
A new AI-driven system called ModGraProDep has been developed to predict cancer survival and risk. This innovative tool uses probabilistic modeling and machine learning to provide personalized predictions for patients, enabling more effective treatment planning and improved patient outcomes.
A new model called Swiftaid has been developed to automate the process of claiming Gift Aid on donations, allowing charities to claim up to £2.50 for every £10 donated. This system uses formal methods to improve design and security, streamlining the process and unlocking millions in extra funding.