Articles tagged with Mathematical Modeling
A new analysis spanning 10,000 years of history and ten major world regions found that world population size, major technological advances, and geographical connectivity are key drivers of military technology evolution. The study's methodology employed a resource called Seshat: Global History Databank to empirically test prior hypotheses.
A team of researchers has developed a novel machine learning model that can identify medication orders requiring pharmacy intervention using provider behavior and contextual features. This approach reduces the risk of exposing sensitive patient data, while alleviating the workload of pharmacists and increasing patient safety.
A new mathematical model suggests that patients with anemia can be effectively treated with transfusions of blood substitutes, which are more readily available than real blood. This approach could reduce the negative effects of blood transfusion and lower costs.
A study of 5,000 simulated undergraduates found that weekly surveillance testing was associated with a substantial reduction in infections. Even at low vaccine effectiveness rates, quarantine protocols were effective in reducing infections by up to 93.6%.
Assistant Professor Kang Hao Cheong and his team discovered that chaotic switching for quantum coin Parrondo's games has similar underlying ideas to encryption. They found that using pre-generated chaotic sequences enhances the work, making it easier to invert the encrypted message to obtain the original state.
A study found that snakes rapidly diversified their diets following the K-Pg mass extinction event, incorporating birds, fish, and small mammals into their diet. This diversification was triggered by ecological opportunities presented by the extinction, with some lineages experiencing further bursts of adaptive evolution.
Researchers find that most countries are not growing wind and solar power fast enough to meet global warming targets of 1.5°C or 2°C. The study analyzes data from 60 countries and finds that only a few countries, like Portugal and Ireland, have seen rapid growth in onshore wind and solar power.
Liquid marbles' unique hydrophobic outer layer allows for faster evaporation than bare water droplets due to particle-particle and liquid-particle interactions. The team's mathematical model accurately predicts evaporation behavior, providing insights into these tiny biological structures.
A team of scientists at NAIST successfully used automatic differentiation to accelerate calculations of model parameter extraction, reducing computation time by 3.5 times compared to conventional methods. This breakthrough enables the design of more efficient power converters with increased performance and reduced energy consumption.
Using observations, lab experiments, theory, and computation, researchers have developed a simple theory to explain the form and growth of apples' cusp-like features. The team found that mechanical instability and underlying fruit anatomy play joint roles in giving rise to multiple cusps in fruits.
Researchers from MIT have developed a way to use carbon dioxide monitors to estimate indoor COVID-19 risk in near real-time. The model takes into account various factors such as ventilation, mask usage, and activity levels to provide an indoor reproductive number that represents the virus's transmission rate.
A new study by University of Cambridge researchers proposes abandoning the R rate for tracking pandemics in favor of nowcasts, which forecast daily new cases and deaths based on growth rates. The Harvey-Kattuman model has already proved accurate and effective in predicting COVID-19 waves and spikes worldwide.
Researchers developed a mathematical model to study the action of SARS-CoV-2, enabling the examination of individual virus particles and their spikes. This analysis can help fine-tune prevention and treatment by providing insights into viral load, spike distribution, and cell infection potential.
Mathematical modeling suggests that contact-tracing apps can help achieve herd immunity by identifying individuals with greater exposure to others and targeting them for vaccination. The approach, known as 'hot-spotting,' could reduce the number of vaccine doses needed by up to half.
Researchers at Rice University have been awarded a four-year, $1.2 million National Institutes of Health grant to advance the art and science of creating custom-designed microbial colonies. The grant will enable the development of technologies like synthetic tissues that enhance soils or gut microbiomes.
Researchers suggest that protecting the microbiome is crucial in preventing and fighting off infections with harmful, drug-resistant bacteria. By preserving a healthy balance of beneficial microorganisms, individuals can reduce their susceptibility to these types of infections.
Researchers developed a mathematical model to predict the angle of repose of sand hills, which is influenced by particle size and gravity. The model was validated using simulations and has potential applications in space exploration, additive manufacturing, and planetary science.
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...
A team of scientists from Incheon National University developed a programmable DNA-based microfluidic chip that can perform complex mathematical calculations, such as Boolean logic operations. The chip uses a motor-operated valve system to execute a series of reactions in rapid and convenient manner.
The UMass Amherst Reich Lab, led by biostatistician Nicholas Reich, will continue to advance real-time forecasting methods during infectious disease outbreaks thanks to a five-year NIH grant renewal. The lab's flu forecasting collaborative has been designated as one of two national Influenza Forecasting Centers of Excellence.
A new study by the ICTA-UAB establishes that populations of hunter-gatherer groups with strong seasonal dependence on meat had fewer people per square kilometer than those relying on abundant plant foods. This result is based on a complex set of calculations showing how diet composition influenced population density.
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.